1. ABA Mouse ISH
  2. ABA Macaque microarray adult/development
  3. ABA Human microarray adult/development
  4. ABA Human RNA-seq adult/development
  5. sc-RNAs-seq reference datasets for deconvolution:
    1. Nathan Skene’s mouse meta-data
    2. Neurogenic cells (Shin et al. 2015)
    3. Human data (Darmanis et al. 2015)


  1. Using Table S1 (or some other curated list), search the ABA for “neurons in waiting.”

  2. Extend the analysis to include human brain. I’m not sure if the gene list from figure 5 in the paper you attached could be used directly (since the samples we collected from human were whole dentate gyrus, if I am remembering correctly), but that would make a good starting point.

  3. A similar option would be to do a deconvolution analysis on RNA-Seq data from the BrainSpan atlas using the overlapping mouse/NHP[/human?] neurogenesis list as marker genes for SGZ. In theory we could show the relative decrease in SGZ neurons over time and also show that it doesn’t reach 0 (as long as we get any reads from these genes in the oldest donors).

  1. Use expression values for all genes in the SGZ (mouse or macaque) or sc-RNA-seq from neural stem cell, and compare to transcriptome expression from each brain region in an ABA atlas.
  1. Look for similar logFC of gene sets for each ‘likelyCellType’ (Miller et al. 2013) (e.g. immature neuron, dividing cell) in one of the above atlases (after differential expression).
  1. Can compare directly within respective species’ ABA atlases
  2. Becomes interspecies when comparing to ABA human (likely diverged)
  1. Using mouse or macaque modules (Miller et al. 2013) (e.g. tan), identify similar modules (based on similar kME values, or gene list overlap) one of the above atlases (after WGNCA) and then find which brain structures express them the most.
  1. Problem: tan module was found by only analyzing 24 SGZ/GCL samples. Unlikely to recover the same tan module with full ABA macaque dataset.
  1. Use sc-RNA-seq gene list to find similar patterns of expression across any of the ABA datasets.

  2. Deconvolution:
  1. EWCE: Using a sc-RNA-seq reference dataset and an ABA atlas.
  2. Other methods: Use a gene list as a reference and ____ ____

Import Datasets

library(pander)
setwd("~/Desktop/Research/Signatures_of_Neurogensis/")

ABA data: Mouse

# Mouse SGZ & GCL: GSE39697

ABA data: Adult Human Microarray

load("/Users/schilder/Desktop/Dissertation/Gene_Expression/Raw data/Microarray/Full_Human_Microarray/All_ABA_humans.mni.RData")
# Set up eset
  sampleInfo.hum <-subset(sampleInfo.hum_all, select= -c(mni_x,mni_y,mni_z))

# Make all cols factors, & Reorder names alphabetically
  sampleInfo.hum <- lapply(sampleInfo.hum, factor)
  sampleInfo.hum <- sampleInfo.hum[order(names(sampleInfo.hum))]

library(annotate)
# Set up phenotype data
  pdata <- AnnotatedDataFrame(data.frame(sampleInfo.hum))
  sampleNames(pdata) <- sampleInfo.hum$unique_id
# Create ExpressionSet
  ABA_hum <- ExpressionSet(datExpr.hum_all, phenoData = pdata) #Correct this annot
# Add treatment condition
  ABA_hum$treatment <- "Human"

ABA data: Developmental Macaque Microarray

Macaque Preprocessing

library(WGCNA)
#mac_expression <- read.csv("Full Macaque Microarray/expression_matrix.csv", header=FALSE, row.names=1)
#mac_sampleinfo <- read.csv("Full Macaque Microarray/columns_metadata.csv", header=TRUE)
#mac_probes <- read.csv("Full Macaque Microarray/rows_metadata.csv", header=TRUE)
#save(mac_expression, mac_sampleinfo, mac_probes, file="Macaque Data.RData")
  # Saved as R objects to speed up importing data
load("~/Desktop/Dissertation/Gene_Expression/Raw data/Microarray/Macaque Data.RData")
datExpr <- mac_expression
probeInfo <- mac_probes
sampleInfo <- mac_sampleinfo
rm(mac_expression,mac_probes,mac_sampleinfo)
# Select only probes with associated entrez genes
  rownames(datExpr) <- probeInfo$probe_name
  validProbes = which(probeInfo$entrez_id>=0,arr.ind=TRUE)
  datExpr <- datExpr[validProbes,]
  probeInfo <- probeInfo[validProbes,]
# Some genes have more than one probe. The collapseRows function chooses a single probe to represent a gene.
probeInfo$gene_symbol <-gsub("[()]","",probeInfo$gene_symbol) # Gets rid of weird parentheses
  probeNames = probeInfo[which(probeInfo$probe_name == rownames(datExpr)),"gene_symbol"]
  probeIDs = rownames(datExpr)
  datCollapsed = WGCNA::collapseRows(datExpr, probeNames, probeIDs) # Collapse
  datExpr <- datCollapsed$datETcollapsed
  probeInfo <- probeInfo[which(datCollapsed$selectedRow==T, arr.ind=T),]
save(datExpr, probeInfo, sampleInfo,file="ABA.macaque_preprocessed.v4.RData")

Convert to eset

load("~/Desktop/Research/Signatures_of_Neurogensis/ABA.macaque_preprocessed.v4.RData")
datExpr.mac <-datExpr
probeInfo.mac <-probeInfo
sampleInfo.mac <-sampleInfo
rm(datExpr,probeInfo,sampleInfo)
library(annotate)
# Create eset
ex_mac <- ExpressionSet(datExpr.mac)
# Create macaque phenoData:
sampleInfo.mac$unique_id <- factor(paste(sampleInfo.mac$donor_name, sampleInfo.mac$structure_acronym, sampleInfo.mac$well_id, sep="_"))

p <- data.frame(sampleInfo.mac)
rownames(p) <- sampleInfo.mac$unique_id
pdata <- AnnotatedDataFrame(p)
# Replace phenoData in mac ExpressionSet
phenoData(ex_mac) <- pdata
# Insert condition
ex_mac$treatment <- "Macaque"

ex_mac <-ex_mac[,colSums(is.na(exprs(ex_mac))) == 0] # Remove cols with NA 

rm(datExpr.mac,probeInfo.mac,sampleInfo.mac,p,pdata)

Import Miller et al. 2013 gene sets

  • Miller J a, Nathanson J, Franjic D, Shim S, Dalley R a, Shapouri S, et al.: Conserved molecular signatures of neurogenesis in the hippocampal subgranular zone of rodents and primates. [Internet]. Development 2013;140:4633–44.
library(readxl)
# S1: Genes differentially expressed between mouse SGZ and GCL
S1 <- data.frame(read_excel("~/Desktop/Research/Signatures_of_Neurogensis/Miller_2013/DEV097212 TableS1.xlsx",na = "NA"))
# Get rid of stars
S1$MouseGene <-gsub("[*]","",S1$MouseGene)

unique(S1$likelyCellType)
## [1] "Immature Neuron"           "Dividing Cell"            
## [3] "Red Blood Cell"            "Vascular Endothelial Cell"
## [5] "Oligodendrocyte"           "Astrocyte"                
## [7] "Hilar Interneuron"         "GABA Neuron"              
## [9] "(undetermined)"
  ## Get gene list
  immature.neuron <- S1[S1$likelyCellType=="Immature Neuron",]$MouseGene
  dividing.cell <- S1[S1$likelyCellType=="Dividing Cell",]$MouseGene
  S1.genes <- S1$MouseGene
  # Convert from mouse to human genes
  library(EWCE)
  data("mouse_to_human_homologs")
  m2h = unique(mouse_to_human_homologs[,c("HGNC.symbol","MGI.symbol")])
  s1.genes <- unique(m2h[m2h$MGI.symbol %in% S1.genes,"HGNC.symbol"])
  
  paste(sum(s1.genes %in% row.names(exprs(ex_mac))),"/",length(S1.genes),
          "'Mouse SGZ-enriched' genes found in ABA Macaque microarray dataset.")
## [1] "250 / 363 'Mouse SGZ-enriched' genes found in ABA Macaque microarray dataset."
# S3: Mouse network modules
mouse_modules <- data.frame(read_excel("~/Desktop/Research/Signatures_of_Neurogensis/Miller_2013/DEV097212 TableS3.xlsx",na = "NA"))
  ## Subset specific modules
  ### tan module was associated with "neurogenesis" in Miller et al. 2013.
Mouse_mod.tan <- subset(mouse_modules, Module=="tan")$HumanOrtholog
  paste(sum(Mouse_mod.tan %in% row.names(exprs(ex_mac))),"/",length(Mouse_mod.tan),"Mouse Tan module genes found in ABA Macaque microarray dataset.")
## [1] "21 / 26 Mouse Tan module genes found in ABA Macaque microarray dataset."
# S4: Macaque network modules
macaque_modules <- data.frame(read_excel("~/Desktop/Research/Signatures_of_Neurogensis/Miller_2013/DEV097212 TableS4.xlsx", na="NA"))
  ## Subset specific modules
  ### tan module was associated with "neurogenesis" in Miller et al. 2013.
  Mac_mod.tan <- subset(macaque_modules, Module=="tan")$Gene
  paste(sum(Mac_mod.tan %in% row.names(exprs(ex_mac))),"/",length( Mac_mod.tan),"Macaque Tan module genes found in ABA Macaque microarray dataset.")
## [1] "159 / 226 Macaque Tan module genes found in ABA Macaque microarray dataset."

Identify Macaque SGZ-enriched genes

# Perform DGE on Macaque SGZ vs. GCL
  SGZ.samples <-ex_mac[,pData(ex_mac)$structure_acronym==c("DGsg") ]
  GCL.samples <-ex_mac[,pData(ex_mac)$structure_acronym==c("DGgr") ]

# Limma
  library(limma); library(statmod)
  limma.data <-cbind(exprs(SGZ.samples), exprs(GCL.samples))
  fit <-lmFit(limma.data, design=
                 c(rep(1,ncol(exprs(SGZ.samples))), rep(-1,ncol(exprs(GCL.samples)))) )
  fit <- eBayes(fit)
  SGZ.vs.GCL.results <- topTable(fit, number=Inf)
  
# Pair-wise t-test
 # t.test(exprs(SGZ.samples), exprs(GCL.samples), paired=T)

sig.DEGS <-row.names(subset(SGZ.vs.GCL.results, adj.P.Val<=0.05))
paste("There are",length(sig.DEGS),"significant DEGs between Macaque SGZ and GCL.")
## [1] "There are 0 significant DEGs between Macaque SGZ and GCL."
# Top logFC genes
topTable(fit, number=10, sort.by = "logFC")
##                logFC   AveExpr          t   P.Value adj.P.Val         B
## PDE7B     -0.9180228  8.713520 -0.7460964 0.4587068 0.9997796 -4.597508
## SLFN13    -0.9097828  9.888308 -0.6544719 0.5154671 0.9997796 -4.598198
## LOC696983 -0.8983702  9.857653 -0.6475556 0.5198988 0.9997796 -4.598247
## SV2B      -0.8928369  8.605551 -0.7263575 0.4706225 0.9997796 -4.597664
## CD36      -0.8237889  6.117680 -0.9247264 0.3590467 0.9997796 -4.595916
## LOC712107 -0.7955258  9.041498 -0.6338976 0.5287094 0.9997796 -4.598341
## ACVR1C    -0.7874374  7.286313 -0.7568770 0.4522729 0.9997796 -4.597422
## C2orf55   -0.7801166 10.330626 -0.5429380 0.5893111 0.9997796 -4.598920
## ANO3      -0.7715562 10.594549 -0.5300162 0.5981805 0.9997796 -4.598995
## PCSK2     -0.7449458  8.575641 -0.6173636 0.5394787 0.9997796 -4.598453

Candidate Genes

library(dplyr); library(ggplot2)
candidate.genes <- ex_mac[row.names(exprs(ex_mac)) %in% c("SOX11","SOX4","DCX","MKI67")][,pData(ex_mac)$age=="48 mo"]

copy.mac <- data.frame(t(data.frame(exprs(candidate.genes))))
#copy.mac$Region <-sub(".*_ *(.*?) *_.*", "\\1", colnames(exprs(candidate.genes)))
copy.mac$structure_name <-pData(candidate.genes)$structure_name
copy.mac$structure_acronym <-pData(candidate.genes)$structure_acronym
candidate.data <-copy.mac %>% group_by(structure_name,structure_acronym) %>% summarise(SOX11=mean(SOX11), DCX=mean(DCX), MKI67=mean(MKI67)) %>% data.frame()



# SOX11
data <-candidate.data[order(-candidate.data$SOX11),]
## All regions
ggplot(data) + geom_bar(aes(x=structure_name, y=SOX11, fill=SOX11),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5))

# Top 10 expression
ggplot(rbind(subset(data,structure_acronym!="DGsg" & structure_acronym!="DGgr")[1:10,], subset(data,structure_acronym=="DGsg"|structure_acronym=="DGgr"))) + geom_bar(aes(x=structure_name, y=SOX11, fill=SOX11),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5))+ labs(title="Regions with the top 10 expression:\nSOX11")

# DCX
data <-candidate.data[order(-candidate.data$DCX),]
## All regions
ggplot(data) + geom_bar(aes(x=structure_name, y=DCX, fill=DCX),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5))

## Top 10 expression
ggplot(rbind(subset(data,structure_acronym!="DGsg" & structure_acronym!="DGgr")[1:10,], subset(data,structure_acronym=="DGsg"|structure_acronym=="DGgr"))) + geom_bar(aes(x=structure_name, y=DCX, fill=DCX),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5))+ labs(title="Regions with the top 10 expression:\nDCX")

# MKI67
data <-candidate.data[order(-candidate.data$MKI67),]
## All regions
ggplot(data) + geom_bar(aes(x=structure_name, y=MKI67, fill=MKI67),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5))

## Top 10 expression
ggplot(rbind(subset(data, structure_acronym!="DGsg" & structure_acronym!="DGgr")[1:10,], subset(data,structure_acronym=="DGsg"|structure_acronym=="DGgr"))) + geom_bar(aes(x=structure_name, y=MKI67, fill=MKI67),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5)) + labs(title="Regions with the top 10 expression:\nMIK67")

[Approach 1: Unsupervised Clustering]

PCA: All Genes

first.PC <-1; last.PC <-4

# Run PCA
  # full_PCA <- prcomp(exprs(ex_mac), scale.=T, center=F)
  # save(full_PCA, file="all.genes_PCA.rda")
  load("all.genes_PCA.rda")
# Get proportion of variance
per_PC1 <- round(summary(full_PCA)$importance[2,1]*100,2)
per_PC2 <- round(summary(full_PCA)$importance[2,2]*100,2)
# Create data.frame
full_PCA.data <- data.frame(full_PCA$rotation[,first.PC:last.PC])
full_PCA.data$Region <- pData(ex_mac)$structure_acronym
full_PCA.data$Age <- factor(pData(ex_mac)$age, c("E40","E50","E70","E80","E90","E120","0 mo","3 mo","12 mo","48 mo"),ordered=T)


# plot PCA
dat <-full_PCA.data
DGsg <-subset(dat,Region=="DGsg")
# By Region
### Plot
library(ggplot2)
ggplot(data=dat) + geom_point(aes(x=PC1, y=PC2, fill=Region, color=Region), size=2) +
  geom_point(data=DGsg, aes(x=PC1, y=PC2, colour=Region), size=4, shape=15) +
  geom_point(data=DGsg, aes(x=PC1, y=PC2), colour="black", size=4, shape=0) + labs(title="PCA: Colored by Region \n[SGZ samples outlined]", x=paste("PC1 (",per_PC1,"%)"), y=paste("PC2 (",per_PC2,"%)")) + theme(plot.title = element_text(hjust=0.5), legend.position="None")

## By Age
ggplot(data=dat) + geom_point(aes(x=PC1, y=PC2, fill=Age, color=Age), size=2) +
  geom_point(data=DGsg, aes(x=PC1, y=PC2,colour=Age), size=4, shape=15) +
  geom_point(data=DGsg, aes(x=PC1, y=PC2),colour="black", size=4, shape=0) + labs(title="PCA: Colored by Age \n[SGZ samples outlined]", x=paste("PC1 (",per_PC1,"%)"), y=paste("PC2 (",per_PC2,"%)")) + theme(plot.title = element_text(hjust=0.5))

# Get top PC1 loadings
PC1.load <-full_PCA$x[order(-abs(full_PCA$x[,1])),][,"PC1"]
write.csv(PC1.load[1:100],"PC1.allgenes.csv")
pander(PC1.load[1:10] , style='simple',justify='left')
Table continues below
LOC701831 RPLP0 LOC711964 RPS16 ACTG1 LOC708858 LOC710110
-79.14 -78.52 -78.51 -78.31 -78.24 -77.7 -77.66
RPS11 LOC697734 EEF1A1
-77.64 -77.61 -77.59

Unsupervised Clustering

  • Interactive Heatmap Instructions:
  • Hover cursor over cells to see info.
  • Click and drag a box over the area you want to zoom in on.
  • Click a column or row label to highlight just that column/row.
  • Double click to return to original view.
library(cluster); library(factoextra); library(magrittr)


# [1: Distance Methods]
unsup.clust_distance <- function(data){
res.dist <- get_dist(data, stand=T, method="pearson")
  library(d3heatmap) # use interactive heatmap instead
d3heatmap(res.dist, scale = "column", k_row=num_clusts, k_col=num_clusts)
#fviz_dist(res.dist, gradient=list(low="#00AFBB", mid="white", high="#FC4E07"))
}

# [2: Partitioning Methods]
# [[K-means clustering]]
unsup.clust_kmeans <-function(data.){
  #library("NbClust")
  #nb <- NbClust(data., distance="euclidean", min.nc=2, max.nc=10, method="kmeans")
  library("factoextra")
  # Conduct kmean using that number
  set.seed(123)
  km.res <- kmeans(data., num_clusts, nstart = 25) # Automatically extract # of clusters
  ## Visualize
  fviz_cluster(km.res, data = data.,
             ellipse.type = "convex",
             palette = "jco",
             ggtheme = theme_minimal(), repel=F)
}
  
# [[Partitioning Around Medoids (PAM) clustering]]
unsup.clust_PAM <-function(data.){
  library("cluster")
  pam.res <- pam(data., num_clusts)
  ## Visualize
  fviz_cluster(pam.res, data = data.,
             ellipse.type = "convex",
             palette = "jco",
             ggtheme = theme_minimal(), repel=T)
}


# [Hierarchical Methods]
# [[Hierarchical Clustering]]
unsup.clust_hierarchical <- function(data){
  library("factoextra")
  res.hc <- data %>%
    scale() %>%                    # Scale the data
    dist(method = "euclidean") %>% # Compute dissimilarity matrix
    hclust(method = "ward.D2")     # Compute hierachical clustering
  # Visualize using factoextra
  # Cut in 4 groups and color by groups
  fviz_dend(res.hc, k = num_clusts, # Cut in (#) groups
            cex = 0.5, repel=F, # label size
            palette="aaas",
            color_labels_by_k =T, # color labels by groups
            rect=T, # Add rectangle around groups
            type="circular", #"rectangle", "circular", "phylogenic".
            phylo_layout="layout.auto",# layout.auto, layout_with_drl, layout_as_tree, layout.gem, layout.mds, layout_with_lgl
            horiz=T, main="Unsupervised Hierarchical Clustering"
            )
  assign("hierarchical.output",res.hc)
}


# [[Hierarchical Clustering on Principal Components (HCPC)]]
unsup.clust_hcpc <- function(facto_data){
  library(FactoMineR)
  # Principal Component Analysis:
  res.pca <-  PCA(facto_data, graph=F)
    plot(res.pca)
  # Clustering, auto nb of clusters:
  hc <- HCPC(res.pca, nb.clust= -1)
    hc$call$t$nb.clust # Number of suggest clusters
  # Construct a hierarchical tree from a partition (with 10 clusters)
    # (useful when the number of individuals is very important)
    # hc2 <- HCPC(iris[,1:4], kk=10, nb.clust=-1)
}

All Genes

# Setup data
    unsup.eset <-ex_mac[,pData(ex_mac)$age=="48 mo"]
    my_data <- t(exprs(unsup.eset))
    row.names(my_data) <-make.unique(as.character(pData(unsup.eset)$structure_name))
    # my_data <-full_PCA.data[,first.PC:last.PC]
    # row.names(my_data) <-make.unique(as.character(full_PCA.data$Region))
# Estimate  number of clusters
    # est.clusts <-fviz_nbclust(my_data, kmeans, method = "gap_stat") # [0]
      # save(est.clusts, file="estimated.clusters_all.genes.rda")
      load("estimated.clusters_all.genes.rda")
      est.clusts

      num_clusts=5 # Select number of clusters based on results
# Conduct unsupervised clustering
     unsup.clust_distance(my_data) # [1]
    # unsup.clust_kmeans(my_data) # [2]
    # unsup.clust_PAM(my_data) #[3]
    # unsup.clust_hierarchical(my_data) #[4]
    # unsup.clust_hcpc(my_data) # [5]

Gene Subset: Mouse SGZ-enriched (Table S1)

# Setup data
unsup.eset <-ex_mac[row.names(exprs(ex_mac)) %in% s1.genes, pData(ex_mac)$age=="48 mo"] 
    my_data <- t(exprs(unsup.eset))
    row.names(my_data) <-make.unique(as.character(pData(unsup.eset)$structure_name))
    # my_data <-full_PCA.data[,first.PC:last.PC]
    # row.names(my_data) <-make.unique(as.character(full_PCA.data$Region))
# Estimate  number of clusters
    est.clusts_S1.genes <-fviz_nbclust(my_data, kmeans, method = "gap_stat") # [0]
    est.clusts_S1.genes

      num_clusts=3 # Select number of clusters based on results
# Conduct unsupervised clustering
    unsup.clust_distance(my_data) # [1]
    # unsup.clust_kmeans(my_data) # [2]
    # unsup.clust_PAM(my_data) #[3]
    # unsup.clust_hierarchical(my_data) #[4]
    # unsup.clust_hcpc(my_data) # [5]

Gene Subset: ‘Macaque Tan Module’ genes only

# Setup data
unsup.eset <-ex_mac[row.names(exprs(ex_mac)) %in% Mac_mod.tan, pData(ex_mac)$age=="48 mo"]
    my_data <- t(exprs(unsup.eset))
    row.names(my_data) <-make.unique(as.character(pData(unsup.eset)$structure_name))
    # my_data <-full_PCA.data[,first.PC:last.PC]
    # row.names(my_data) <-make.unique(as.character(full_PCA.data$Region))
# Estimate  number of clusters
    est.clusts_S4.genes <-fviz_nbclust(my_data, kmeans, method = "gap_stat") # [0]
    est.clusts_S4.genes

      num_clusts=5 # Select number of clusters based on results
# Conduct unsupervised clustering
    unsup.clust_distance(my_data) # [1]
    # unsup.clust_kmeans(my_data) # [2]
    # unsup.clust_PAM(my_data) #[3]
    # unsup.clust_hierarchical(my_data) #[4]
    # unsup.clust_hcpc(my_data) # [5]

[Approach 2: Compare Expression Profiles]

Differential Expression

All Genes

library(limma); library(statmod)
SGZ.samples <-exprs(ex_mac[,pData(ex_mac)$structure_acronym==c("DGsg")& pData(ex_mac)$age=="48 mo"])

region_list <- as.character(unique(pData(ex_mac)$structure_name))
DGE_results <- NULL
DGE_summary <- data.frame()
for(region in region_list){
   region.samples <-exprs( ex_mac[,pData(ex_mac)$structure_name==region & pData(ex_mac)$age=="48 mo"] )
   limma.data <-cbind(SGZ.samples, region.samples)
    fit <-lmFit(limma.data, design=
                 c(rep(1,ncol(SGZ.samples)), rep(-1,ncol(region.samples))) )
    fit <- eBayes(fit)
    tab <- topTable(fit, number=Inf)
   DGE_results[[paste("SGZ vs.",region)]] <- list(tab)
   # Create summary table
   sig <-dim(subset(tab, adj.P.Val<=0.05))[1]
   total <- dim(tab)[1]
   percent_DEGs <-round(dim(subset(tab, adj.P.Val<=0.05))[1] / 
     dim(tab)[1] *100,3)
   DGE_summary <- rbind(DGE_summary, 
                        data.frame(Test=paste("SGZ vs.", region), Sig_DEGs=sig,
                             Total_genes=total, Percent_DEGs=round(sig/total*100,3),
                             Total_logFC=sum(abs(tab$logFC)),
                             Total_t.stat=sum(abs(tab$t))) 
                        )
}

# Print written summary
no_DEGs <-length(subset(DGE_summary, Sig_DEGs==0)$Sig_DEGs)
total_regions <-length(DGE_summary$Test)
paste(no_DEGs,"/",total_regions,"adult brain regions contained no Differentially Expressed Genes compared to adult SGZ.")
## [1] "61 / 128 adult brain regions contained no Differentially Expressed Genes compared to adult SGZ."
# Results table
dge <-subset(DGE_summary,Test!="<NA>")
pander(dge[order(c(dge$percent_DEGs, dge$Total_logFC)),], style='simple', justify='left', split.table=Inf)
  Test Sig_DEGs Total_genes Percent_DEGs Total_logFC Total_t.stat
69 SGZ vs. subgranular zone of dentate gyrus (cortex) 0 17555 0 8.799e-12 2.409e-12
73 SGZ vs. granular layer of dentate gyrus (cortex) 0 17555 0 1579 433.1
70 SGZ vs. stratum pyramidale of CA3 0 17555 0 3443 941.2
80 SGZ vs. polyform layer of dentate gyrus (cortex) 0 17555 0 3464 946.3
60 SGZ vs. stratum pyramidale of CA1 0 17555 0 3500 957.1
58 SGZ vs. stratum pyramidale of CA2 0 17555 0 3594 983.3
101 SGZ vs. subiculum 0 17555 0 3746 1024
74 SGZ vs. lateral nucleus 0 17555 0 4555 1236
104 SGZ vs. rostral periamygdaloid cortex (rPAC) 0 17555 0 4602 1249
44 SGZ vs. paralaminar nucleus 0 17555 0 4610 1251
103 SGZ vs. amygdalopiriform transition area 0 17555 0 4747 1287
106 SGZ vs. amygdalohippocampal area 0 17555 0 4823 1310
43 SGZ vs. stratum radiatum of CA1 0 17555 0 4840 1326
46 SGZ vs. accessory basal nucleus (basomedial nucleus) 0 17555 0 4890 1328
65 SGZ vs. medial nucleus 0 17555 0 4994 1353
105 SGZ vs. anterior amygdaloid area 0 17555 0 5193 1410
47 SGZ vs. basal nucleus (basolateral nucleus) 0 17555 0 5385 1463
107 SGZ vs. central amygdaloid nucleus 0 17555 0 5398 1468
90 SGZ vs. layer II of V1 0 17555 0 5675 1584
89 SGZ vs. layer III of V1 0 17555 0 5745 1602
98 SGZ vs. layer IVA of V1 0 17555 0 5798 1618
110 SGZ vs. layer II of V2 0 17555 0 5895 1644
114 SGZ vs. layer III of V2 0 17555 0 5918 1650
92 SGZ vs. layer II of rostral cingulate cortex 0 17555 0 5942 1659
68 SGZ vs. layer V of V1 0 17555 0 5955 1663
66 SGZ vs. layer V of rostral cingulate cortex 0 17555 0 5969 1665
112 SGZ vs. layer V of V2 0 17555 0 6009 1676
122 SGZ vs. layer V of caudal orbitofrontal cortex 0 17555 0 6019 1680
113 SGZ vs. layer VI of V2 0 17555 0 6032 1683
124 SGZ vs. layer II of dorsolateral prefrontal cortex 0 17555 0 6035 1683
100 SGZ vs. olfactory tubercle 0 17555 0 6037 1689
95 SGZ vs. layer IVCa of V1 0 17555 0 6104 1705
120 SGZ vs. layer II of caudal orbitofrontal cortex 0 17555 0 6107 1705
121 SGZ vs. layer III of caudal orbitofrontal cortex 0 17555 0 6132 1713
51 SGZ vs. layer VI of rostral cingulate cortex 0 17555 0 6139 1710
127 SGZ vs. layer V of dorsolateral prefrontal cortex 0 17555 0 6144 1715
96 SGZ vs. layer IVB of V1 0 17555 0 6159 1719
111 SGZ vs. granular layer IV of V2 0 17555 0 6166 1720
125 SGZ vs. layer III of dorsolateral prefrontal cortex 0 17555 0 6168 1722
71 SGZ vs. layer VI of V1 0 17555 0 6185 1729
126 SGZ vs. granular layer IV of dorsolateral prefrontal cortex 0 17555 0 6189 1728
108 SGZ vs. layer I of V1 0 17555 0 6191 1727
117 SGZ vs. granular layer IV of medial orbitofrontal cortex 0 17555 0 6237 1743
9 SGZ vs. putamen 0 17555 0 6271 1754
93 SGZ vs. layer III of rostral cingulate cortex 0 17555 0 6303 1761
99 SGZ vs. islands of Calleja 0 17555 0 6309 1765
128 SGZ vs. layer VI of dorsolateral prefrontal cortex 0 17555 0 6310 1761
94 SGZ vs. layer IVCb of V1 0 17555 0 6320 1766
123 SGZ vs. layer VI of caudal orbitofrontal cortex 0 17555 0 6413 1793
116 SGZ vs. layer III of medial orbitofrontal cortex 0 17555 0 6421 1796
118 SGZ vs. layer V of medial orbitofrontal cortex 0 17555 0 6429 1800
119 SGZ vs. layer VI of medial orbitofrontal cortex 0 17555 0 6525 1824
115 SGZ vs. layer II of medial orbitofrontal cortex 0 17555 0 6592 1848
31 SGZ vs. external segment of globus pallidus 0 17555 0 7077 1982
30 SGZ vs. internal segment of globus pallidus 0 17555 0 7728 2168
33 SGZ vs. internal capsule 0 17555 0 7758 2176
109 SGZ vs. white matter of V1 0 17555 0 8284 2322
102 SGZ vs. CA4 region 0 17555 0 27339 6825
91 SGZ vs. stratum oriens of CA1 0 17555 0 27349 6833
10 SGZ vs. nucleus accumbens 0 17555 0 29612 7546
3 SGZ vs. caudate nucleus 0 17555 0 29669 7567
1 SGZ vs. ventricular zone of V1 17555 17555 100 136550 1320635
2 SGZ vs. caudal ganglionic eminence 17555 17555 100 136550 1320635
4 SGZ vs. globus pallidus 17555 17555 100 136550 1320635
5 SGZ vs. cortical hem 17555 17555 100 136550 1320635
6 SGZ vs. medial ganglionic eminence 17555 17555 100 136550 1320635
7 SGZ vs. lateral ganglionic eminence 17555 17555 100 136550 1320635
8 SGZ vs. hippocampal subventricular zone of CA1 17555 17555 100 136550 1320635
11 SGZ vs. inner ventricular zone of rostral cingulate cortex 17555 17555 100 136550 1320635
12 SGZ vs. outer ventricular zone of rostral cingulate cortex 17555 17555 100 136550 1320635
13 SGZ vs. subventricular zone of rostral cingulate cortex 17555 17555 100 136550 1320635
14 SGZ vs. marginal zone of rostral cingulate cortex 17555 17555 100 136550 1320635
15 SGZ vs. hippocampal ventricular zone of CA1 17555 17555 100 136550 1320635
16 SGZ vs. subventricular zone of V1 17555 17555 100 136550 1320635
17 SGZ vs. marginal zone of CA1 17555 17555 100 136550 1320635
18 SGZ vs. amygdaloid complex 17555 17555 100 136550 1320635
19 SGZ vs. inner ventricular zone of V1 17555 17555 100 136550 1320635
20 SGZ vs. outer ventricular zone of V1 17555 17555 100 136550 1320635
21 SGZ vs. inner cortical plate (infragranular layer) of V1 17555 17555 100 136550 1320635
22 SGZ vs. marginal zone of V1 17555 17555 100 136550 1320635
23 SGZ vs. intermediate zone of V1 17555 17555 100 136550 1320635
24 SGZ vs. subplate zone of V1 17555 17555 100 136550 1320635
25 SGZ vs. dorsal lateral geniculate nucleus 17555 17555 100 136550 1320635
26 SGZ vs. dentate migratory stream 17555 17555 100 136550 1320635
27 SGZ vs. hippocampal subplate of CA1 17555 17555 100 136550 1320635
28 SGZ vs. hippocampal plate of CA1 17555 17555 100 136550 1320635
29 SGZ vs. granular layer anlage of dentate gyrus (cortex) 17555 17555 100 136550 1320635
32 SGZ vs. lateral ganglionic eminence-cortex border 17555 17555 100 136550 1320635
34 SGZ vs. outer fiber (plexiform) zone of rostral cingulate cortex 17555 17555 100 136550 1320635
35 SGZ vs. inner ventricular zone of S1 17555 17555 100 136550 1320635
36 SGZ vs. outer ventricular zone of S1 17555 17555 100 136550 1320635
37 SGZ vs. subventricular zone of S1 17555 17555 100 136550 1320635
38 SGZ vs. subplate zone of S1 17555 17555 100 136550 1320635
39 SGZ vs. inner cortical plate (infragranular layer) of S1 17555 17555 100 136550 1320635
40 SGZ vs. intermediate zone of rostral cingulate cortex 17555 17555 100 136550 1320635
41 SGZ vs. subplate zone of rostral cingulate cortex 17555 17555 100 136550 1320635
42 SGZ vs. inner cortical plate (infragranular layer) of rostral cingulate cortex 17555 17555 100 136550 1320635
45 SGZ vs. medial division of central nucleus 17555 17555 100 136550 1320635
48 SGZ vs. outer subventricular zone of rostral cingulate cortex 17555 17555 100 136550 1320635
49 SGZ vs. inner fiber (plexiform) zone of rostral cingulate cortex 17555 17555 100 136550 1320635
50 SGZ vs. outer cortical plate of rostral cingulate cortex 17555 17555 100 136550 1320635
52 SGZ vs. outer fiber (plexiform) zone of V1 17555 17555 100 136550 1320635
53 SGZ vs. inner subventricular zone of V1 17555 17555 100 136550 1320635
54 SGZ vs. outer subventricular zone of V1 17555 17555 100 136550 1320635
55 SGZ vs. intermediate cell dense zone of V1 17555 17555 100 136550 1320635
56 SGZ vs. pyramidal layer of subiculum 17555 17555 100 136550 1320635
57 SGZ vs. hippocampal intermediate zone of CA1 17555 17555 100 136550 1320635
59 SGZ vs. stratum lacunosum-moleculare of CA1 17555 17555 100 136550 1320635
61 SGZ vs. inner subventricular zone of S1 17555 17555 100 136550 1320635
62 SGZ vs. outer subventricular zone of S1 17555 17555 100 136550 1320635
63 SGZ vs. layer VI of S1 17555 17555 100 136550 1320635
64 SGZ vs. amygdaloid intramedullary gray 17555 17555 100 136550 1320635
67 SGZ vs. outer cortical plate of V1 17555 17555 100 136550 1320635
72 SGZ vs. lateral division of central nucleus 17555 17555 100 136550 1320635
75 SGZ vs. transitory migratory zone of V1 17555 17555 100 136550 1320635
76 SGZ vs. inner subventricular zone of rostral cingulate cortex 17555 17555 100 136550 1320635
77 SGZ vs. cortical plate of V1 17555 17555 100 136550 1320635
78 SGZ vs. ventricular zone of S1 17555 17555 100 136550 1320635
79 SGZ vs. dorsal lateral geniculate nucleus, parvocellular layers 17555 17555 100 136550 1320635
81 SGZ vs. inner fiber (plexiform) zone of V1 17555 17555 100 136550 1320635
82 SGZ vs. supragranular layer of V1 17555 17555 100 136550 1320635
83 SGZ vs. granular layer IV of V1 17555 17555 100 136550 1320635
84 SGZ vs. periamygdaloid cortex (cortical amygdaloid nucleus) 17555 17555 100 136550 1320635
85 SGZ vs. ventricular zone of rostral cingulate cortex 17555 17555 100 136550 1320635
86 SGZ vs. molecular layer of dentate gyrus (cortex) 17555 17555 100 136550 1320635
87 SGZ vs. dorsal lateral geniculate nucleus, magnocellular layers 17555 17555 100 136550 1320635
88 SGZ vs. outer cortical plate of S1 17555 17555 100 136550 1320635
97 SGZ vs. dorsal lateral geniculate nucleus, koniocellular layers 17555 17555 100 136550 1320635

Mouse SGZ-enriched genes (Table S1)

#Subset just the candidate genes and re-run t-tests
  ## Dividing.Cell genes (Miller et al. 2013, Table S1)
  gene.sub <- ex_mac[row.names(exprs(ex_mac)) %in% s1.genes, pData(ex_mac)$age=="48 mo"]
  
SGZ.samples <-exprs(gene.sub[,pData(gene.sub)$structure_acronym==c("DGsg")])

region_list <- as.character(unique(pData(gene.sub)$structure_name))
DGE_results <- NULL
DGE_summary <- data.frame()
for(region in region_list){
   region.samples <-exprs( gene.sub[,pData(gene.sub)$structure_name==region] )
   limma.data <-cbind(SGZ.samples, region.samples)
    fit <-lmFit(limma.data, design=
                 c(rep(1,ncol(SGZ.samples)), rep(-1,ncol(region.samples))) )
    fit <- eBayes(fit)
    tab <- topTable(fit, number=Inf)
   DGE_results[[paste("SGZ vs.",region)]] <- list(tab)
   # Create summary table
   sig <-dim(subset(tab, adj.P.Val<=0.05))[1]
   total <- dim(tab)[1]
   percent_DEGs <-round(dim(subset(tab, adj.P.Val<=0.05))[1] / 
     dim(tab)[1] *100,3)
   DGE_summary <- rbind(DGE_summary, 
                        data.frame(Test=paste("SGZ vs.", region), Sig_DEGs=sig,
                             Total_genes=total, Percent_DEGs=round(sig/total*100,3),
                             Total_logFC=sum(abs(tab$logFC)),
                             Total_t.stat=sum(abs(tab$t))) 
                        )
}

# Print written summary
no_DEGs <-length(subset(DGE_summary, Sig_DEGs==0)$Sig_DEGs)
total_regions <-length(DGE_summary$Test)
paste(no_DEGs,"/",total_regions,"adult brain regions contained no Differentially Expressed Genes compared to adult SGZ.")
## [1] "61 / 61 adult brain regions contained no Differentially Expressed Genes compared to adult SGZ."
# Results table
dge <-subset(DGE_summary,Test!="<NA>")
pander(dge[order(c(dge$percent_DEGs, dge$Total_logFC)),], style='simple', justify='left', split.table=Inf)
  Test Sig_DEGs Total_genes Percent_DEGs Total_logFC Total_t.stat
49 SGZ vs. subgranular zone of dentate gyrus (cortex) 0 250 0 1.327e-13 3.338e-14
44 SGZ vs. granular layer of dentate gyrus (cortex) 0 250 0 59.48 15.26
50 SGZ vs. stratum pyramidale of CA3 0 250 0 75.55 18.69
46 SGZ vs. stratum pyramidale of CA1 0 250 0 75.68 18.83
42 SGZ vs. stratum pyramidale of CA2 0 250 0 77.22 19.13
3 SGZ vs. layer V of rostral cingulate cortex 0 250 0 93.68 23.53
45 SGZ vs. subiculum 0 250 0 94.2 23.06
52 SGZ vs. paralaminar nucleus 0 250 0 97.19 23.74
16 SGZ vs. layer V of caudal orbitofrontal cortex 0 250 0 97.84 24.51
8 SGZ vs. layer V of dorsolateral prefrontal cortex 0 250 0 98.73 24.8
17 SGZ vs. layer VI of caudal orbitofrontal cortex 0 250 0 99.15 24.84
18 SGZ vs. layer VI of dorsolateral prefrontal cortex 0 250 0 100.5 25.05
1 SGZ vs. layer II of rostral cingulate cortex 0 250 0 100.5 25.22
2 SGZ vs. layer III of rostral cingulate cortex 0 250 0 100.7 25.33
58 SGZ vs. amygdalohippocampal area 0 250 0 100.9 24.53
12 SGZ vs. layer V of medial orbitofrontal cortex 0 250 0 101.4 25.49
23 SGZ vs. olfactory tubercle 0 250 0 101.6 25.5
4 SGZ vs. layer VI of rostral cingulate cortex 0 250 0 102 25.34
11 SGZ vs. granular layer IV of medial orbitofrontal cortex 0 250 0 102.3 25.73
13 SGZ vs. layer VI of medial orbitofrontal cortex 0 250 0 102.3 25.48
15 SGZ vs. layer III of caudal orbitofrontal cortex 0 250 0 103 25.96
14 SGZ vs. layer II of caudal orbitofrontal cortex 0 250 0 103.7 25.97
54 SGZ vs. lateral nucleus 0 250 0 104.1 25.34
7 SGZ vs. granular layer IV of dorsolateral prefrontal cortex 0 250 0 104.6 26.27
55 SGZ vs. amygdalopiriform transition area 0 250 0 105.8 25.64
6 SGZ vs. layer III of dorsolateral prefrontal cortex 0 250 0 106.1 26.66
41 SGZ vs. layer VI of V2 0 250 0 106.2 26.48
56 SGZ vs. rostral periamygdaloid cortex (rPAC) 0 250 0 106.4 25.76
10 SGZ vs. layer III of medial orbitofrontal cortex 0 250 0 106.4 26.76
9 SGZ vs. layer II of medial orbitofrontal cortex 0 250 0 106.7 26.84
57 SGZ vs. accessory basal nucleus (basomedial nucleus) 0 250 0 107.2 26.02
5 SGZ vs. layer II of dorsolateral prefrontal cortex 0 250 0 107.4 26.83
33 SGZ vs. layer V of V1 0 250 0 107.7 26.9
39 SGZ vs. layer V of V2 0 250 0 107.9 26.91
22 SGZ vs. putamen 0 250 0 108.2 27.21
47 SGZ vs. stratum radiatum of CA1 0 250 0 109.4 26.62
34 SGZ vs. layer VI of V1 0 250 0 109.5 27.42
43 SGZ vs. polyform layer of dentate gyrus (cortex) 0 250 0 109.7 26.49
27 SGZ vs. layer II of V1 0 250 0 110.4 27.65
28 SGZ vs. layer III of V1 0 250 0 110.6 27.7
36 SGZ vs. layer II of V2 0 250 0 111.1 27.74
59 SGZ vs. basal nucleus (basolateral nucleus) 0 250 0 112.2 27.25
38 SGZ vs. granular layer IV of V2 0 250 0 112.9 28.21
29 SGZ vs. layer IVA of V1 0 250 0 113.6 28.46
37 SGZ vs. layer III of V2 0 250 0 114 28.45
21 SGZ vs. islands of Calleja 0 250 0 114 28.49
60 SGZ vs. central amygdaloid nucleus 0 250 0 119.4 28.92
30 SGZ vs. layer IVB of V1 0 250 0 119.8 29.9
32 SGZ vs. layer IVCb of V1 0 250 0 120.5 30.24
53 SGZ vs. medial nucleus 0 250 0 122 29.5
31 SGZ vs. layer IVCa of V1 0 250 0 122.7 30.83
26 SGZ vs. layer I of V1 0 250 0 123.6 30.36
51 SGZ vs. anterior amygdaloid area 0 250 0 127.9 30.84
24 SGZ vs. external segment of globus pallidus 0 250 0 131.6 32.63
20 SGZ vs. internal capsule 0 250 0 135.9 34.08
25 SGZ vs. internal segment of globus pallidus 0 250 0 143.2 35.73
35 SGZ vs. white matter of V1 0 250 0 153.4 38.4
48 SGZ vs. stratum oriens of CA1 0 250 0 353.2 77.83
61 SGZ vs. CA4 region 0 250 0 396.4 89.38
40 SGZ vs. nucleus accumbens 0 250 0 417.1 95.1
19 SGZ vs. caudate nucleus 0 250 0 429.5 98.66

Macaque Tan module genes (Table S4)

#Subset just the candidate genes and re-run t-tests
  ## Dividing.Cell genes (Miller et al. 2013, Table S4)
  gene.sub <- ex_mac[row.names(exprs(ex_mac)) %in% Mac_mod.tan, pData(ex_mac)$age=="48 mo"]
  
SGZ.samples <-exprs(gene.sub[,pData(gene.sub)$structure_acronym==c("DGsg")])

region_list <- as.character(unique(pData(gene.sub)$structure_name))
DGE_results <- NULL
DGE_summary <- data.frame()
for(region in region_list){
   region.samples <-exprs( gene.sub[,pData(gene.sub)$structure_name==region] )
   limma.data <-cbind(SGZ.samples, region.samples)
    fit <-lmFit(limma.data, design=
                 c(rep(1,ncol(SGZ.samples)), rep(-1,ncol(region.samples))) )
    fit <- eBayes(fit)
    tab <- topTable(fit, number=Inf)
   DGE_results[[paste("SGZ vs.",region)]] <- list(tab)
   # Create summary table
   sig <-dim(subset(tab, adj.P.Val<=0.05))[1]
   total <- dim(tab)[1]
   percent_DEGs <-round(dim(subset(tab, adj.P.Val<=0.05))[1] / 
     dim(tab)[1] *100,3)
   DGE_summary <- rbind(DGE_summary, 
                        data.frame(Test=paste("SGZ vs.", region), Sig_DEGs=sig,
                             Total_genes=total, Percent_DEGs=round(sig/total*100,3),
                             Total_logFC=sum(abs(tab$logFC)),
                             Total_t.stat=sum(abs(tab$t))) 
                        )
}

# Print written summary
no_DEGs <-length(subset(DGE_summary, Sig_DEGs==0)$Sig_DEGs)
total_regions <-length(DGE_summary$Test)
paste(no_DEGs,"/",total_regions,"adult brain regions contained no Differentially Expressed Genes compared to adult SGZ.")
## [1] "61 / 61 adult brain regions contained no Differentially Expressed Genes compared to adult SGZ."
# Results table
dge <-subset(DGE_summary,Test!="<NA>")
pander(dge[order(c(dge$percent_DEGs, dge$Total_logFC)),], style='simple', justify='left', split.table=Inf)
  Test Sig_DEGs Total_genes Percent_DEGs Total_logFC Total_t.stat
49 SGZ vs. subgranular zone of dentate gyrus (cortex) 0 159 0 8.449e-14 2.223e-14
44 SGZ vs. granular layer of dentate gyrus (cortex) 0 159 0 24.2 6.45
50 SGZ vs. stratum pyramidale of CA3 0 159 0 44.14 11.54
58 SGZ vs. amygdalohippocampal area 0 159 0 45.72 11.87
56 SGZ vs. rostral periamygdaloid cortex (rPAC) 0 159 0 46.88 12.15
42 SGZ vs. stratum pyramidale of CA2 0 159 0 47.34 12.39
55 SGZ vs. amygdalopiriform transition area 0 159 0 47.79 12.37
46 SGZ vs. stratum pyramidale of CA1 0 159 0 48.55 12.72
54 SGZ vs. lateral nucleus 0 159 0 49.34 12.8
57 SGZ vs. accessory basal nucleus (basomedial nucleus) 0 159 0 49.57 12.87
53 SGZ vs. medial nucleus 0 159 0 52.54 13.55
45 SGZ vs. subiculum 0 159 0 52.66 13.68
26 SGZ vs. layer I of V1 0 159 0 53.09 13.92
23 SGZ vs. olfactory tubercle 0 159 0 53.83 14.3
22 SGZ vs. putamen 0 159 0 54.07 14.38
43 SGZ vs. polyform layer of dentate gyrus (cortex) 0 159 0 55 14.06
21 SGZ vs. islands of Calleja 0 159 0 55.16 14.58
14 SGZ vs. layer II of caudal orbitofrontal cortex 0 159 0 55.4 14.79
52 SGZ vs. paralaminar nucleus 0 159 0 55.69 14.34
5 SGZ vs. layer II of dorsolateral prefrontal cortex 0 159 0 56.15 14.94
1 SGZ vs. layer II of rostral cingulate cortex 0 159 0 56.49 15.11
51 SGZ vs. anterior amygdaloid area 0 159 0 57.02 14.68
59 SGZ vs. basal nucleus (basolateral nucleus) 0 159 0 57.44 14.89
47 SGZ vs. stratum radiatum of CA1 0 159 0 58.26 14.99
60 SGZ vs. central amygdaloid nucleus 0 159 0 58.29 15.02
36 SGZ vs. layer II of V2 0 159 0 58.35 15.56
9 SGZ vs. layer II of medial orbitofrontal cortex 0 159 0 58.61 15.7
27 SGZ vs. layer II of V1 0 159 0 58.88 15.73
4 SGZ vs. layer VI of rostral cingulate cortex 0 159 0 59.43 15.61
3 SGZ vs. layer V of rostral cingulate cortex 0 159 0 60.32 16.12
13 SGZ vs. layer VI of medial orbitofrontal cortex 0 159 0 60.78 16.03
10 SGZ vs. layer III of medial orbitofrontal cortex 0 159 0 61.23 16.41
2 SGZ vs. layer III of rostral cingulate cortex 0 159 0 61.98 16.63
12 SGZ vs. layer V of medial orbitofrontal cortex 0 159 0 62.69 16.76
28 SGZ vs. layer III of V1 0 159 0 62.84 16.78
11 SGZ vs. granular layer IV of medial orbitofrontal cortex 0 159 0 63.19 16.9
18 SGZ vs. layer VI of dorsolateral prefrontal cortex 0 159 0 63.24 16.66
17 SGZ vs. layer VI of caudal orbitofrontal cortex 0 159 0 63.36 16.83
33 SGZ vs. layer V of V1 0 159 0 63.39 16.84
39 SGZ vs. layer V of V2 0 159 0 63.48 16.84
32 SGZ vs. layer IVCb of V1 0 159 0 63.72 16.92
37 SGZ vs. layer III of V2 0 159 0 64.09 17.09
41 SGZ vs. layer VI of V2 0 159 0 64.13 16.92
16 SGZ vs. layer V of caudal orbitofrontal cortex 0 159 0 64.13 17.08
15 SGZ vs. layer III of caudal orbitofrontal cortex 0 159 0 64.33 17.31
29 SGZ vs. layer IVA of V1 0 159 0 64.35 17.15
8 SGZ vs. layer V of dorsolateral prefrontal cortex 0 159 0 65.17 17.39
6 SGZ vs. layer III of dorsolateral prefrontal cortex 0 159 0 65.35 17.52
7 SGZ vs. granular layer IV of dorsolateral prefrontal cortex 0 159 0 65.45 17.48
31 SGZ vs. layer IVCa of V1 0 159 0 66 17.55
30 SGZ vs. layer IVB of V1 0 159 0 66.17 17.57
38 SGZ vs. granular layer IV of V2 0 159 0 66.18 17.58
34 SGZ vs. layer VI of V1 0 159 0 66.39 17.62
24 SGZ vs. external segment of globus pallidus 0 159 0 67.85 17.82
25 SGZ vs. internal segment of globus pallidus 0 159 0 75.87 19.96
20 SGZ vs. internal capsule 0 159 0 76.19 19.98
35 SGZ vs. white matter of V1 0 159 0 81.26 21.1
48 SGZ vs. stratum oriens of CA1 0 159 0 231.5 54.13
61 SGZ vs. CA4 region 0 159 0 253 60.37
40 SGZ vs. nucleus accumbens 0 159 0 264.1 63.77
19 SGZ vs. caudate nucleus 0 159 0 269.1 65.27

[Approach 3: Compare Network Modules]

WGCNA: Macaque Microarray (SGZ/GCL samples only, all ages)

Attempt to recreate networks from Miller et al. 2013

  library(WGCNA)
## ==========================================================================
## *
## *  Package WGCNA 1.61 loaded.
## *
## *    Important note: It appears that your system supports multi-threading,
## *    but it is not enabled within WGCNA in R. 
## *    To allow multi-threading within WGCNA with all available cores, use 
## *
## *          allowWGCNAThreads()
## *
## *    within R. Use disableWGCNAThreads() to disable threading if necessary.
## *    Alternatively, set the following environment variable on your system:
## *
## *          ALLOW_WGCNA_THREADS=<number_of_processors>
## *
## *    for example 
## *
## *          ALLOW_WGCNA_THREADS=8
## *
## *    To set the environment variable in linux bash shell, type 
## *
## *           export ALLOW_WGCNA_THREADS=8
## *
## *     before running R. Other operating systems or shells will
## *     have a similar command to achieve the same aim.
## *
## ==========================================================================
  library(flashClust)
allowWGCNAThreads() # Enable multi-threading
## Allowing multi-threading with up to 8 threads.
all.SGZ.GCL <- ex_mac[,pData(ex_mac)$structure_acronym %in% c("DGsg","DGgr")]
datExpr <- exprs(all.SGZ.GCL)
#net_corrs <- function(eset1, eset2){


## Soft threshold
powers = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14) 
sft = pickSoftThreshold(datExpr, networkType="signed", powerVector=powers) # t(net$MEs)
##    Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1      1    0.205 58.80        0.30000    48.4      48.4   48.8
## 2      2    0.215 30.10        0.32600    46.9      46.9   47.7
## 3      3    0.232 21.10        0.34000    45.4      45.5   46.6
## 4      4    0.258 17.30        0.36700    44.0      44.1   45.5
## 5      5    0.422 15.00        0.66000    42.7      42.8   44.5
## 6      6    0.427 12.70        0.65900    41.4      41.5   43.5
## 7      7    0.433 11.00        0.65000    40.2      40.3   42.5
## 8      8    0.438  9.74        0.64800    39.0      39.2   41.5
## 9      9    0.455  8.90        0.64900    37.8      38.1   40.6
## 10    10    0.462  8.18        0.63200    36.7      37.0   39.7
## 11    11    0.466  7.51        0.62900    35.7      36.0   38.8
## 12    12    0.477  7.12        0.60100    34.7      35.0   37.9
## 13    13    0.607  7.99        0.65800    33.7      34.0   37.1
## 14    14    0.219 31.60        0.00827    32.8      33.1   36.2
### Plot powers
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n",
main = paste("Scale independence"))
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
labels=powers,cex=0.9,col="red");

# this line corresponds to using an R^2 cut-off of h
  #abline(h=0.9,col="red")

## Run
POWER=14 # Same as Miller et al. 2013
# Assume defaults for everything not mentioned in Miller et al. 2013
   #net.mac.SGZ_GCL = blockwiseModules(t(datExpr), power=POWER, networkType="signed")
 #save(net.mac.SGZ_GCL, file="WGCNA_ABA.macaque_SGZ.GCL.rda")

#___________________________________________________________________________________#
# Plot WGCNA results
load("~/Desktop/Research/Signatures_of_Neurogensis/WGCNA_ABA.macaque_SGZ.GCL.rda")
# Plot
num_mods <- length(colnames(net.mac.SGZ_GCL$MEs))
moduleColorsAutomatic = WGCNA::labels2colors(net.mac.SGZ_GCL$colors)
mColors = moduleColorsAutomatic[net.mac.SGZ_GCL$blockGenes[[1]]]
plotDendroAndColors(net.mac.SGZ_GCL$dendrograms[[1]], colors=mColors, dendroLabels=F, 
                    groupLabels=c(paste("deepSplit = 2\n# modules =",num_mods)), addGuide=T,main="WGCNA Modules:\n Macaque SGZ & GCL samples")

# Get mutually exclusive modules assignments for each gene
mod_assignments <-data.frame(ModuleColors=net.mac.SGZ_GCL$colors, genes=row.names(datExpr))
mod_assignments <- mod_assignments[order(mod_assignments$ModuleColors),]
write.csv(mod_assignments, "module.gene.assignments_SGZ.GCL.csv")

Rough ‘% overlap with genes in Tan module’ metric

modules <-unique(mod_assignments$ModuleColors)
module.overlap_summary <-data.frame()

for(color in modules){
  one.module <-subset(mod_assignments,ModuleColors==color)
  percent_tan.overlap <-round(sum(Mac_mod.tan %in% as.character(one.module$genes)) / length(one.module$genes)*100,3)
  module.overlap_summary <- rbind(module.overlap_summary,data.frame(New_Module=color, New_Module_Size=length(one.module$genes), Percent_chance.overlap=length(one.module$genes)/dim(exprs(ex_mac))[1]*100, Percent_Tan.overlap=percent_tan.overlap))
}

pander::pander(module.overlap_summary[order(-module.overlap_summary$Percent_Tan.overlap),],style='simple',justify='left', split.table=Inf)
  New_Module New_Module_Size Percent_chance.overlap Percent_Tan.overlap
19 magenta 476 2.711 13.87
32 sienna3 38 0.2165 7.895
7 darkmagenta 51 0.2905 7.843
15 grey60 146 0.8317 2.74
4 cyan 243 1.384 2.469
12 green 1021 5.816 2.449
22 orange 87 0.4956 2.299
17 lightgreen 142 0.8089 2.113
31 salmon 301 1.715 1.993
30 saddlebrown 67 0.3817 1.493
33 skyblue 67 0.3817 1.493
5 darkgreen 97 0.5525 1.031
13 greenyellow 389 2.216 0.771
14 grey 285 1.623 0.702
36 tan 340 1.937 0.588
27 purple 406 2.313 0.493
3 brown 1110 6.323 0.45
1 black 742 4.227 0.404
2 blue 1237 7.046 0.404
28 red 1014 5.776 0.296
40 yellow 1058 6.027 0.189
37 turquoise 6166 35.12 0.162
6 darkgrey 87 0.4956 0
8 darkolivegreen 51 0.2905 0
9 darkorange 81 0.4614 0
10 darkred 115 0.6551 0
11 darkturquoise 94 0.5355 0
16 lightcyan 157 0.8943 0
18 lightyellow 134 0.7633 0
20 mediumpurple3 23 0.131 0
21 midnightblue 231 1.316 0
23 orangered4 30 0.1709 0
24 paleturquoise 65 0.3703 0
25 pink 575 3.275 0
26 plum1 31 0.1766 0
29 royalblue 128 0.7291 0
34 skyblue3 34 0.1937 0
35 steelblue 66 0.376 0
38 violet 57 0.3247 0
39 white 77 0.4386 0
41 yellowgreen 36 0.2051 0

WGCNA: Macaque Microarray (all regions, all genes, all ages)

Create new networks using all samples

 datExpr <- exprs(ex_mac)
#net_corrs <- function(eset1, eset2){
  library(WGCNA)
  library(flashClust)

## Soft threshold
powers = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14) 
sft = pickSoftThreshold(datExpr, networkType="signed", powerVector=powers) # t(net$MEs)
##    Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1      1    0.819 99.40          0.922    1770      1770   1790
## 2      2    0.820 50.20          0.939    1690      1690   1740
## 3      3    0.816 33.40          0.936    1610      1620   1680
## 4      4    0.811 25.00          0.934    1540      1550   1630
## 5      5    0.810 19.70          0.939    1470      1480   1570
## 6      6    0.801 16.80          0.945    1400      1420   1520
## 7      7    0.796 14.40          0.944    1340      1360   1470
## 8      8    0.793 12.60          0.944    1280      1300   1430
## 9      9    0.783 11.10          0.937    1230      1250   1380
## 10    10    0.786 10.50          0.945    1180      1200   1340
## 11    11    0.783  9.49          0.946    1130      1150   1300
## 12    12    0.776  8.62          0.944    1080      1100   1260
## 13    13    0.773  7.92          0.942    1030      1060   1220
## 14    14    0.769  7.33          0.941     991      1020   1180
### Plot powers
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n",
main = paste("Scale independence"))
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
labels=powers,cex=0.9,col="red");

# this line corresponds to using an R^2 cut-off of h
  #abline(h=0.9,col="red")

## Run
POWER=14 # Same as Miller et al. 2013
# Assume defaults for everything not mentioned in Miller et al. 2013
  #net.mac = blockwiseModules(t(datExpr), power=POWER, networkType="signed", deepSplit=2)
# save(net.mac, file="WGCNA_ABA.macaque_all.samples.rda")

#___________________________________________________________________________________#
# Plot WGCNA results
load("~/Desktop/Research/Signatures_of_Neurogensis/WGCNA_ABA.macaque_all.samples.rda")
# Plot
num_mods <- length(colnames(net.mac$MEs))
moduleColorsAutomatic = WGCNA::labels2colors(net.mac$colors)
mColors = moduleColorsAutomatic[net.mac$blockGenes[[1]]]
plotDendroAndColors(net.mac$dendrograms[[1]], colors=mColors, dendroLabels=F, 
                    groupLabels=c(paste("deepSplit = 2\n# modules =",num_mods)), addGuide=T,main="Cluster Dendrogram")

# Get mutually exclusive modules assignments for each gene
mod_assignments <-data.frame(ModuleColors=net.mac$colors, genes=row.names(datExpr))
mod_assignments <-mod_assignments[order(mod_assignments$ModuleColors),]
write.csv(mod_assignments, "module.gene.assignments_all.regions.csv")

Rough ‘% overlap with genes in Tan module’ metric

modules <-unique(mod_assignments$ModuleColors)
module.overlap_summary <-data.frame()

for(color in modules){
  one.module <-subset(mod_assignments,ModuleColors==color)
  percent_tan.overlap <-round(sum(Mac_mod.tan %in% as.character(one.module$genes)) / length(one.module$genes)*100,3)
  module.overlap_summary <- rbind(module.overlap_summary,data.frame(New_Module=color, New_Module_Size=length(one.module$genes), Percent_chance.overlap=length(one.module$genes)/dim(exprs(ex_mac))[1]*100, Percent_Tan.overlap=percent_tan.overlap))
}

pander::pander(module.overlap_summary[order(-module.overlap_summary$Percent_Tan.overlap),],style='simple',justify='left', split.table=Inf)
  New_Module New_Module_Size Percent_chance.overlap Percent_Tan.overlap
12 green 997 5.679 4.714
27 red 656 3.737 4.116
22 orangered4 41 0.2336 2.439
18 lightyellow 232 1.322 2.155
10 darkred 196 1.116 2.041
14 grey 493 2.808 2.028
8 darkolivegreen 108 0.6152 1.852
13 greenyellow 378 2.153 1.852
21 orange 169 0.9627 1.775
23 paleturquoise 140 0.7975 1.429
34 steelblue 141 0.8032 1.418
25 plum1 75 0.4272 1.333
38 white 164 0.9342 1.22
16 lightcyan 252 1.435 1.19
39 yellow 1059 6.032 0.944
19 magenta 435 2.478 0.92
37 violet 136 0.7747 0.735
9 darkorange 168 0.957 0.595
6 darkgrey 174 0.9912 0.575
1 black 609 3.469 0.493
15 grey60 250 1.424 0.4
24 pink 519 2.956 0.385
20 midnightblue 272 1.549 0.368
4 cyan 289 1.646 0.346
36 turquoise 3413 19.44 0.293
3 brown 1450 8.26 0.276
2 blue 2188 12.46 0.183
5 darkgreen 192 1.094 0
7 darkmagenta 99 0.5639 0
11 darkturquoise 176 1.003 0
17 lightgreen 244 1.39 0
26 purple 409 2.33 0
28 royalblue 227 1.293 0
29 saddlebrown 159 0.9057 0
30 salmon 293 1.669 0
31 sienna3 90 0.5127 0
32 skyblue 162 0.9228 0
33 skyblue3 82 0.4671 0
35 tan 329 1.874 0
40 yellowgreen 89 0.507 0
  • NOTES: There’s almost no overlap between the new modules (derived from all macaque samples) and the tan module (derived from only macaque SGZ/GCL samples).

Network Preservation Analysis

# Subset all samples from SGZ or GCL
all.SGZ.GCL <- ex_mac[,pData(ex_mac)$structure_acronym %in% c("DGsg","DGgr")]
# Setup up inputs
multiExpr  = list(A1=list(data=t(exprs(ex_mac))), A2=list(data=t(exprs(all.SGZ.GCL))) )
multiColor = list(A1 = net.mac$colors)
# Run network preservation
mp <- modulePreservation(multiExpr, multiColor, referenceNetworks=1, verbose=3, networkType="signed", nPermutations=30, maxGoldModuleSize=100)
save(mp, file="network.preservation_all.samples.vs.GCL.SGZ.rda")

Plot network preservation scores

load("~/Desktop/Research/Signatures_of_Neurogensis/network.preservation_all.samples.vs.GCL.SGZ.rda")
# Summary stats
stats <- mp$preservation$Z$ref.A1$inColumnsAlsoPresentIn.A2 # All stats output
pander::pander(stats[order(-stats[,2]),c(1:2)][1:10,],style='simple',justify='left')
  moduleSize Zsummary.pres
yellow 1000 40.12
turquoise 1000 29.84
brown 1000 26.32
magenta 326 25.12
green 770 22.88
red 435 21.55
lightcyan 179 19.75
lightyellow 162 18.31
grey60 173 16.48
lightgreen 163 15.11
 # Plot summary stats (Zsummary.pres)
  library(ggplot2)
ggplot(stats, aes(y=Zsummary.pres, x=factor(rownames(stats)),
                                 fill=factor(rownames(stats))) ) +
  geom_bar(stat="identity") + labs(title=paste("Network preservation scores"), y="Zsummary.pres", x="Network module") + theme(legend.position="none",axis.text.x = element_text(angle=45, hjust=1)) + scale_fill_manual(values=rownames(stats))

[Approach 4: Deconvolution]

Format scRNAseq reference database

  • Redo with a more relevant sc-RNA-seq reference database
# [Method 1]  
  gse2 <- getGEO("GSE71485", GSEMatrix=T) # imports 0 features...
  #show(gse2)

# [Method 2]
  library(GEOquery)
  filePaths <-getGEOSuppFiles("GSE71485")
  gse <-read.table(row.names(filePaths))
  
library(Biobase)
Biobase::exprs(gse2) <-data.frame(gse2)
gse.eset <-ExpressionSet(gse2, annotation ="hgu133plus2.db" )
library(EWCE)
  
Shin <- read_celltype_data("~/Desktop/Research/Signatures_of_Neurogensis/scRNAseq_datasets/GSE71485_Single_TPM.txt")



library(annotate)
# Create eset
ex_mac <- ExpressionSet(datExpr.mac)
# Create macaque phenoData:
sampleInfo.mac$unique_id <- factor(paste(sampleInfo.mac$donor_name, sampleInfo.mac$structure_acronym, sampleInfo.mac$well_id, sep="_"))

p <- data.frame(sampleInfo.mac)
rownames(p) <- sampleInfo.mac$unique_id
pdata <- AnnotatedDataFrame(p)
# Replace phenoData in mac ExpressionSet
phenoData(ex_mac) <- pdata
# Insert condition
ex_mac$treatment <- "Macaque"

ex_mac <-ex_mac[,colSums(is.na(exprs(ex_mac))) == 0] # Remove cols with NA

‘Mouse SGZ-enriched’ genes (Table S1)

Setup data

# Set up gene list
example_genelist <- S1$MouseGene

library("EWCE")
load(file="~/Desktop/Research/Deconvolution_of_Evolution/celltype_data.rda")
# Find mouse homologues of human genes
  data("mouse_to_human_homologs")
  m2h = unique(mouse_to_human_homologs[,c("HGNC.symbol","MGI.symbol")])
  mouse.hits = unique(m2h[m2h$MGI.symbol %in% example_genelist,"MGI.symbol"])
  mouse.bg  = unique(setdiff(m2h$MGI.symbol,mouse.hits))
  
paste(length(mouse.hits),"/",length(example_genelist),"candidate genes found in sc-RNA-seq reference database.")
## [1] "341 / 363 candidate genes found in sc-RNA-seq reference database."

Sub-cell type level

# Bootstrap significance testing:
## 1) Without controlling for transcript length and GC content
full_results.sub = bootstrap.enrichment.test(sct_data=celltype_data,
                                         mouse.hits=mouse.hits,mouse.bg=mouse.bg,
                                         reps=10000, sub=T)
## [1] "interneurons_Int10"
## [1] 0
## [1] "Fold enrichment: 2.19482408258267"
## [1] "Standard deviations from mean: 14.0130691674156"
## [1] ""
## [1] "interneurons_Int6"
## [1] 0
## [1] "Fold enrichment: 1.85257520115396"
## [1] "Standard deviations from mean: 11.2158270271469"
## [1] ""
## [1] "interneurons_Int9"
## [1] 0
## [1] "Fold enrichment: 1.95878786651468"
## [1] "Standard deviations from mean: 9.54077432172045"
## [1] ""
## [1] "interneurons_Int2"
## [1] 1e-04
## [1] "Fold enrichment: 1.5275445211573"
## [1] "Standard deviations from mean: 7.04677513191367"
## [1] ""
## [1] "interneurons_Int4"
## [1] 0.006
## [1] "Fold enrichment: 1.2205531572426"
## [1] "Standard deviations from mean: 2.85248747675033"
## [1] ""
## [1] "interneurons_Int1"
## [1] 0
## [1] "Fold enrichment: 1.81674537423394"
## [1] "Standard deviations from mean: 8.71008017983052"
## [1] ""
## [1] "interneurons_Int3"
## [1] 0
## [1] "Fold enrichment: 2.17136793226558"
## [1] "Standard deviations from mean: 12.5208453228451"
## [1] ""
## [1] "interneurons_Int13"
## [1] 0
## [1] "Fold enrichment: 1.8108479968661"
## [1] "Standard deviations from mean: 9.96500593937373"
## [1] ""
## [1] "interneurons_Int16"
## [1] 0
## [1] "Fold enrichment: 1.74998659424822"
## [1] "Standard deviations from mean: 11.0864251611685"
## [1] ""
## [1] "interneurons_Int14"
## [1] 0
## [1] "Fold enrichment: 1.53123958374113"
## [1] "Standard deviations from mean: 7.60370208974289"
## [1] ""
## [1] "interneurons_Int11"
## [1] 0
## [1] "Fold enrichment: 1.5821441869313"
## [1] "Standard deviations from mean: 6.45783327361339"
## [1] ""
## [1] "interneurons_Int5"
## [1] 0
## [1] "Fold enrichment: 2.21581828559141"
## [1] "Standard deviations from mean: 16.1995906005206"
## [1] ""
## [1] "interneurons_Int7"
## [1] 0
## [1] "Fold enrichment: 2.10807282777085"
## [1] "Standard deviations from mean: 15.2154122822341"
## [1] ""
## [1] "interneurons_Int8"
## [1] 0
## [1] "Fold enrichment: 1.62523845934985"
## [1] "Standard deviations from mean: 9.85586312608527"
## [1] ""
## [1] "interneurons_Int12"
## [1] 0
## [1] "Fold enrichment: 1.59812107721362"
## [1] "Standard deviations from mean: 8.34306235492575"
## [1] ""
## [1] "interneurons_Int15"
## [1] 0
## [1] "Fold enrichment: 1.53414431066053"
## [1] "Standard deviations from mean: 7.10081498697409"
## [1] ""
## [1] "pyramidal.SS_.none."
## [1] 1
## [1] ""
## [1] "pyramidal.SS_S1PyrL4"
## [1] 0.9792
## [1] ""
## [1] "pyramidal.SS_ClauPyr"
## [1] 0.9713
## [1] ""
## [1] "pyramidal.SS_S1PyrL5"
## [1] 1
## [1] ""
## [1] "pyramidal.SS_S1PyrL23"
## [1] 1
## [1] ""
## [1] "pyramidal.SS_S1PyrDL"
## [1] 0.9896
## [1] ""
## [1] "pyramidal.SS_S1PyrL5a"
## [1] 0.9944
## [1] ""
## [1] "pyramidal.CA1_SubPyr"
## [1] 0.7803
## [1] ""
## [1] "pyramidal.CA1_CA1Pyr1"
## [1] 1
## [1] ""
## [1] "pyramidal.SS_S1PyrL6b"
## [1] 0.8845
## [1] ""
## [1] "pyramidal.SS_S1PyrL6"
## [1] 0.9976
## [1] ""
## [1] "pyramidal.CA1_CA1Pyr2"
## [1] 1
## [1] ""
## [1] "pyramidal.CA1_CA1PyrInt"
## [1] 0.0057
## [1] "Fold enrichment: 1.17966982801049"
## [1] "Standard deviations from mean: 2.80456658085347"
## [1] ""
## [1] "pyramidal.CA1_CA2Pyr2"
## [1] 1
## [1] ""
## [1] "oligodendrocytes_Oligo1"
## [1] 0
## [1] "Fold enrichment: 1.78923077344274"
## [1] "Standard deviations from mean: 5.92568939282575"
## [1] ""
## [1] "oligodendrocytes_Oligo3"
## [1] 1e-04
## [1] "Fold enrichment: 1.5404012022018"
## [1] "Standard deviations from mean: 4.94273793567662"
## [1] ""
## [1] "oligodendrocytes_Oligo4"
## [1] 0
## [1] "Fold enrichment: 1.7024076579995"
## [1] "Standard deviations from mean: 6.88929124822889"
## [1] ""
## [1] "oligodendrocytes_Oligo2"
## [1] 0
## [1] "Fold enrichment: 1.5783799579134"
## [1] "Standard deviations from mean: 6.24861232193111"
## [1] ""
## [1] "oligodendrocytes_Oligo6"
## [1] 0
## [1] "Fold enrichment: 1.52821395738866"
## [1] "Standard deviations from mean: 6.44720172805403"
## [1] ""
## [1] "oligodendrocytes_Oligo5"
## [1] 0
## [1] "Fold enrichment: 1.49461265330592"
## [1] "Standard deviations from mean: 4.98914296686808"
## [1] ""
## [1] "microglia_Mgl1"
## [1] 0.0095
## [1] "Fold enrichment: 1.52911835533135"
## [1] "Standard deviations from mean: 2.63953062938961"
## [1] ""
## [1] "microglia_Mgl2"
## [1] 0.0405
## [1] "Fold enrichment: 1.38818640475282"
## [1] "Standard deviations from mean: 1.93286556306214"
## [1] ""
## [1] "microglia_Pvm1"
## [1] 0.4283
## [1] ""
## [1] "microglia_Pvm2"
## [1] 0.5602
## [1] ""
## [1] "endothelial.mural_Vsmc"
## [1] 0.4418
## [1] ""
## [1] "endothelial.mural_Vend2"
## [1] 0.471
## [1] ""
## [1] "endothelial.mural_Peric"
## [1] 0.3748
## [1] ""
## [1] "endothelial.mural_.none."
## [1] 0.5677
## [1] ""
## [1] "endothelial.mural_Vend1"
## [1] 0.4315
## [1] ""
## [1] "astrocytes.ependymal_Astro2"
## [1] 0
## [1] "Fold enrichment: 3.31168988565184"
## [1] "Standard deviations from mean: 18.1467356540887"
## [1] ""
## [1] "astrocytes.ependymal_Astro1"
## [1] 0
## [1] "Fold enrichment: 3.59260253988707"
## [1] "Standard deviations from mean: 16.5398102591399"
## [1] ""
## [1] "astrocytes.ependymal_.none."
## [1] 0
## [1] "Fold enrichment: 2.35255686707315"
## [1] "Standard deviations from mean: 11.9206737398829"
## [1] ""
## [1] "astrocytes.ependymal_Choroid"
## [1] 0.034
## [1] "Fold enrichment: 1.380293127569"
## [1] "Standard deviations from mean: 2.06452642472585"
## [1] ""
## [1] "astrocytes.ependymal_Epend"
## [1] 0.1421
## [1] ""
full_results.sub$results[order(full_results.sub$results$p),3:5][1:6,]
##                    p fold_change sd_from_mean
## interneurons_Int10 0    2.194824    14.013069
## interneurons_Int6  0    1.852575    11.215827
## interneurons_Int9  0    1.958788     9.540774
## interneurons_Int1  0    1.816745     8.710080
## interneurons_Int3  0    2.171368    12.520845
## interneurons_Int13 0    1.810848     9.965006
# Bootstrap plot
ewce.plot(full_results.sub$results,mtc_method="BH")

## 2) Controlling for transcript length and GC content (NOT WORKING)
### Need to convert gene list to human gene symbols
human.hits = unique(m2h[m2h$HGNC.symbol %in% example_genelist,"HGNC.symbol"])
human.bg = unique(setdiff(m2h$HGNC.symbol,human.hits))
# Bootstrap significance testing controlling for transcript length and GC content
#cont_results.sub = bootstrap.enrichment.test(sct_data=celltype_data,
 #                               human.hits=human.hits, human.bg=human.bg,
 #                                reps=10000, sub=T, geneSizeControl=T)
#ewce.plot(cont_results.sub$results,mtc_method="BH")
  • NOTES: EWCE deconvolution pretty nicely reconstructs cell types from ‘mouse SGZ-enriched’ genes.

Cell type level

# 1) Bootstrap significance testing without controlling for transcript length and GC content
full_results = bootstrap.enrichment.test(sct_data=celltype_data,
                                         mouse.hits=mouse.hits,mouse.bg=mouse.bg,
                                         reps=10000, sub=F)
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 2.09136588810587"
## [1] "Standard deviations from mean: 10.0845867070809"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.4713
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.78847125730897"
## [1] "Standard deviations from mean: 16.8515136328113"
## [1] ""
## [1] "microglia"
## [1] 0.0889
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.6166351855409"
## [1] "Standard deviations from mean: 7.25210931801897"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.9995
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
ewce.plot(full_results$results ,mtc_method="BH")

# 1) Controlling for transcript length and GC content (NOT WORKING)
#cont_results = bootstrap.enrichment.test(sct_data=celltype_data,human.hits=human.hits,human.bg=human.bg,reps=reps,sub=F,geneSizeControl=T)
#ewce.plot(cont_results$results,mtc_method="BH")

‘Macaque tan module’ genes (Table S4)

Setup data

# Set up gene list
example_genelist <- Mac_mod.tan

library("EWCE")
load(file="~/Desktop/Research/Deconvolution_of_Evolution/celltype_data.rda")
# Find mouse homologues of human genes
  data("mouse_to_human_homologs")
  m2h = unique(mouse_to_human_homologs[,c("HGNC.symbol","MGI.symbol")])
  mouse.hits = unique(m2h[m2h$HGNC.symbol %in% example_genelist,"MGI.symbol"])
  mouse.bg  = unique(setdiff(m2h$MGI.symbol,mouse.hits))
  
paste(length(mouse.hits),"/",length(example_genelist),"candidate genes found in sc-RNA-seq reference database.")
## [1] "214 / 226 candidate genes found in sc-RNA-seq reference database."

Sub-cell type level

# Bootstrap significance testing:
## 1) Without controlling for transcript length and GC content
full_results.sub = bootstrap.enrichment.test(sct_data=celltype_data,
                                         mouse.hits=mouse.hits,mouse.bg=mouse.bg,
                                         reps=10000, sub=T)
## [1] "interneurons_Int10"
## [1] 0.0022
## [1] "Fold enrichment: 1.37542231348334"
## [1] "Standard deviations from mean: 3.52907489412147"
## [1] ""
## [1] "interneurons_Int6"
## [1] 0.0388
## [1] "Fold enrichment: 1.19093877316789"
## [1] "Standard deviations from mean: 1.95952748583597"
## [1] ""
## [1] "interneurons_Int9"
## [1] 0.0954
## [1] ""
## [1] "interneurons_Int2"
## [1] 0.9201
## [1] ""
## [1] "interneurons_Int4"
## [1] 0.0752
## [1] ""
## [1] "interneurons_Int1"
## [1] 0.456
## [1] ""
## [1] "interneurons_Int3"
## [1] 0.6313
## [1] ""
## [1] "interneurons_Int13"
## [1] 0.4101
## [1] ""
## [1] "interneurons_Int16"
## [1] 0.0361
## [1] "Fold enrichment: 1.15770545822542"
## [1] "Standard deviations from mean: 1.84888589055422"
## [1] ""
## [1] "interneurons_Int14"
## [1] 0.0371
## [1] "Fold enrichment: 1.1677106808156"
## [1] "Standard deviations from mean: 1.91397854372934"
## [1] ""
## [1] "interneurons_Int11"
## [1] 0.0149
## [1] "Fold enrichment: 1.29210555920499"
## [1] "Standard deviations from mean: 2.54605410331782"
## [1] ""
## [1] "interneurons_Int5"
## [1] 0.0955
## [1] ""
## [1] "interneurons_Int7"
## [1] 0
## [1] "Fold enrichment: 1.46479711630888"
## [1] "Standard deviations from mean: 5.05665145687398"
## [1] ""
## [1] "interneurons_Int8"
## [1] 0.0198
## [1] "Fold enrichment: 1.16908842841573"
## [1] "Standard deviations from mean: 2.13828346706708"
## [1] ""
## [1] "interneurons_Int12"
## [1] 0.0115
## [1] "Fold enrichment: 1.22101604795294"
## [1] "Standard deviations from mean: 2.48330083181076"
## [1] ""
## [1] "interneurons_Int15"
## [1] 0.0892
## [1] ""
## [1] "pyramidal.SS_.none."
## [1] 0.0447
## [1] "Fold enrichment: 1.11291761699782"
## [1] "Standard deviations from mean: 1.71485786508396"
## [1] ""
## [1] "pyramidal.SS_S1PyrL4"
## [1] 0.1121
## [1] ""
## [1] "pyramidal.SS_ClauPyr"
## [1] 0.0435
## [1] "Fold enrichment: 1.25962438397466"
## [1] "Standard deviations from mean: 1.84169436784738"
## [1] ""
## [1] "pyramidal.SS_S1PyrL5"
## [1] 0.0802
## [1] ""
## [1] "pyramidal.SS_S1PyrL23"
## [1] 0.3145
## [1] ""
## [1] "pyramidal.SS_S1PyrDL"
## [1] 0.0532
## [1] ""
## [1] "pyramidal.SS_S1PyrL5a"
## [1] 0.1013
## [1] ""
## [1] "pyramidal.CA1_SubPyr"
## [1] 0.0757
## [1] ""
## [1] "pyramidal.CA1_CA1Pyr1"
## [1] 0.0205
## [1] "Fold enrichment: 1.16088323218451"
## [1] "Standard deviations from mean: 2.13445904485584"
## [1] ""
## [1] "pyramidal.SS_S1PyrL6b"
## [1] 0.2159
## [1] ""
## [1] "pyramidal.SS_S1PyrL6"
## [1] 0.0045
## [1] "Fold enrichment: 1.27575337447446"
## [1] "Standard deviations from mean: 3.26656900134757"
## [1] ""
## [1] "pyramidal.CA1_CA1Pyr2"
## [1] 0.2676
## [1] ""
## [1] "pyramidal.CA1_CA1PyrInt"
## [1] 0.0022
## [1] "Fold enrichment: 1.26042242174832"
## [1] "Standard deviations from mean: 3.24988576786636"
## [1] ""
## [1] "pyramidal.CA1_CA2Pyr2"
## [1] 0.274
## [1] ""
## [1] "oligodendrocytes_Oligo1"
## [1] 1e-04
## [1] "Fold enrichment: 1.86496184531441"
## [1] "Standard deviations from mean: 5.25564195094678"
## [1] ""
## [1] "oligodendrocytes_Oligo3"
## [1] 0
## [1] "Fold enrichment: 1.87659487116985"
## [1] "Standard deviations from mean: 6.41764405238795"
## [1] ""
## [1] "oligodendrocytes_Oligo4"
## [1] 0
## [1] "Fold enrichment: 1.70396316071085"
## [1] "Standard deviations from mean: 5.42162517490978"
## [1] ""
## [1] "oligodendrocytes_Oligo2"
## [1] 0
## [1] "Fold enrichment: 1.85850423772716"
## [1] "Standard deviations from mean: 7.33243443335636"
## [1] ""
## [1] "oligodendrocytes_Oligo6"
## [1] 0
## [1] "Fold enrichment: 1.59232254861078"
## [1] "Standard deviations from mean: 5.71570894832624"
## [1] ""
## [1] "oligodendrocytes_Oligo5"
## [1] 5e-04
## [1] "Fold enrichment: 1.53884964555229"
## [1] "Standard deviations from mean: 4.22554544618381"
## [1] ""
## [1] "microglia_Mgl1"
## [1] 0.0138
## [1] "Fold enrichment: 1.67487138682249"
## [1] "Standard deviations from mean: 2.63425387999052"
## [1] ""
## [1] "microglia_Mgl2"
## [1] 0.08
## [1] ""
## [1] "microglia_Pvm1"
## [1] 0.4666
## [1] ""
## [1] "microglia_Pvm2"
## [1] 0.223
## [1] ""
## [1] "endothelial.mural_Vsmc"
## [1] 0.1736
## [1] ""
## [1] "endothelial.mural_Vend2"
## [1] 0.0228
## [1] "Fold enrichment: 1.40967116962043"
## [1] "Standard deviations from mean: 2.21956100232789"
## [1] ""
## [1] "endothelial.mural_Peric"
## [1] 0.0174
## [1] "Fold enrichment: 1.50660371475035"
## [1] "Standard deviations from mean: 2.46302995459228"
## [1] ""
## [1] "endothelial.mural_.none."
## [1] 0.0647
## [1] ""
## [1] "endothelial.mural_Vend1"
## [1] 0.0023
## [1] "Fold enrichment: 1.76123299275408"
## [1] "Standard deviations from mean: 3.54840899717605"
## [1] ""
## [1] "astrocytes.ependymal_Astro2"
## [1] 0
## [1] "Fold enrichment: 1.98488688740207"
## [1] "Standard deviations from mean: 6.21280188504832"
## [1] ""
## [1] "astrocytes.ependymal_Astro1"
## [1] 0.0016
## [1] "Fold enrichment: 1.79600659835762"
## [1] "Standard deviations from mean: 3.98774558875209"
## [1] ""
## [1] "astrocytes.ependymal_.none."
## [1] 0
## [1] "Fold enrichment: 1.92448169917057"
## [1] "Standard deviations from mean: 6.5041993182344"
## [1] ""
## [1] "astrocytes.ependymal_Choroid"
## [1] 0.0139
## [1] "Fold enrichment: 1.60908586487711"
## [1] "Standard deviations from mean: 2.63564990314784"
## [1] ""
## [1] "astrocytes.ependymal_Epend"
## [1] 0.106
## [1] ""
full_results.sub$results[order(full_results.sub$results$p),3:5][1:6,]
##                             p fold_change sd_from_mean
## interneurons_Int7           0    1.464797     5.056651
## oligodendrocytes_Oligo3     0    1.876595     6.417644
## oligodendrocytes_Oligo4     0    1.703963     5.421625
## oligodendrocytes_Oligo2     0    1.858504     7.332434
## oligodendrocytes_Oligo6     0    1.592323     5.715709
## astrocytes.ependymal_Astro2 0    1.984887     6.212802
# Bootstrap plot
ewce.plot(full_results.sub$results,mtc_method="BH")

## 2) Controlling for transcript length and GC content (NOT WORKING)
### Need to convert gene list to human gene symbols
human.hits = unique(m2h[m2h$HGNC.symbol %in% example_genelist,"HGNC.symbol"])
human.bg = unique(setdiff(m2h$HGNC.symbol,human.hits))
# Bootstrap significance testing controlling for transcript length and GC content
#cont_results.sub = bootstrap.enrichment.test(sct_data=celltype_data,
 #                               human.hits=human.hits, human.bg=human.bg,
 #                                reps=10000, sub=T, geneSizeControl=T)
#ewce.plot(cont_results.sub$results,mtc_method="BH")
  • NOTES: EWCE deconvolution pretty nicely reconstructs cell types from ‘mouse SGZ-enriched’ genes.

Cell type level

# 1) Bootstrap significance testing without controlling for transcript length and GC content
full_results = bootstrap.enrichment.test(sct_data=celltype_data,
                                         mouse.hits=mouse.hits,mouse.bg=mouse.bg,
                                         reps=10000, sub=F)
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.6344116649678"
## [1] "Standard deviations from mean: 4.60910793868818"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.0018
## [1] "Fold enrichment: 1.4747216946214"
## [1] "Standard deviations from mean: 3.24823103769571"
## [1] ""
## [1] "interneurons"
## [1] 0.0066
## [1] "Fold enrichment: 1.14732797782528"
## [1] "Standard deviations from mean: 2.48761425945757"
## [1] ""
## [1] "microglia"
## [1] 0.0737
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.75891797699607"
## [1] "Standard deviations from mean: 7.06881439891116"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.0374
## [1] "Fold enrichment: 1.1169673151793"
## [1] "Standard deviations from mean: 1.80018737393311"
## [1] ""
## [1] "pyramidal SS"
## [1] 0.0142
## [1] "Fold enrichment: 1.1382324582905"
## [1] "Standard deviations from mean: 2.24844729720863"
## [1] ""
ewce.plot(full_results$results ,mtc_method="BH")

# 1) Controlling for transcript length and GC content (NOT WORKING)
#cont_results = bootstrap.enrichment.test(sct_data=celltype_data,human.hits=human.hits,human.bg=human.bg,reps=reps,sub=F,geneSizeControl=T)
#ewce.plot(cont_results$results,mtc_method="BH")

Iteratively run EWCE on each region’s transcriptome

Run DGE: Each region vs. all other regions

samples <-ex_mac[,pData(ex_mac)$age=="48 mo"]

region_list <- as.character(unique(pData(samples)$structure_acronym))
DGE_results <- NULL
DGE_summary <- data.frame()
for(region in region_list){
    other.samples <-exprs( samples[,pData(samples)$structure_acronym!=region] )
   region.samples <-exprs( samples[,pData(samples)$structure_acronym==region] )
  # Run DGE
   limma.data <-cbind(region.samples, other.samples)
    fit <-lmFit(limma.data, design=
                 c(rep(1,ncol(region.samples)), rep(-1,ncol(other.samples))) )
    fit <- eBayes(fit)
    tab <- topTable(fit, number=Inf)
    tab <- tab[order(-abs(tab$logFC)),]
  
   DGE_results[[paste(region,"vs. all other regions")]] <- list(tab)
   # Create summary table
   sig <-dim(subset(tab, adj.P.Val<=0.05))[1]
   total <- dim(tab)[1]
   percent_DEGs <-round(sig / total *100,3)
   DGE_summary <- rbind(DGE_summary,data.frame(Test=paste(region,"vs. all other regions"),Sig_DEGs=sig, Total_genes=total, Percent_DEGs=round(sig/total*100,3),
                             Total_logFC=sum(abs(subset(tab, adj.P.Val<=0.05)$logFC)) ) 
                        )
}

# Print written summary
per_DEG <-length(subset(DGE_summary, percent_DEGs==100)$Sig_DEGs)
total_regions <-length(DGE_summary$Test)
paste(per_DEG,"/",total_regions,"adult brain regions have ALL genes differentially expressed.")
## [1] "61 / 61 adult brain regions have ALL genes differentially expressed."
# Results table
pander::pander(subset(DGE_summary[order(c(DGE_summary$Percent_DEGs, -DGE_summary$Total_logFC)),], Test!="<NA>"),style='simple', justify='left', split.table=Inf)
Test Sig_DEGs Total_genes Percent_DEGs Total_logFC
rCG2 vs. all other regions 17555 17555 100 128972
rCG3 vs. all other regions 17555 17555 100 128975
rCG5 vs. all other regions 17555 17555 100 128966
rCG6 vs. all other regions 17555 17555 100 128954
dlPF2 vs. all other regions 17555 17555 100 128969
dlPF3 vs. all other regions 17555 17555 100 128971
dlPF4 vs. all other regions 17555 17555 100 128970
dlPF5 vs. all other regions 17555 17555 100 128970
mOF2 vs. all other regions 17555 17555 100 129003
mOF3 vs. all other regions 17555 17555 100 128990
mOF4 vs. all other regions 17555 17555 100 128985
mOF5 vs. all other regions 17555 17555 100 128994
mOF6 vs. all other regions 17555 17555 100 128982
cOF2 vs. all other regions 17555 17555 100 128977
cOF3 vs. all other regions 17555 17555 100 128978
cOF5 vs. all other regions 17555 17555 100 128973
cOF6 vs. all other regions 17555 17555 100 128982
dlPF6 vs. all other regions 17555 17555 100 128971
Ca vs. all other regions 17555 17555 100 130460
ic vs. all other regions 17555 17555 100 129026
IsCj vs. all other regions 17555 17555 100 129014
Pu vs. all other regions 17555 17555 100 129009
Tu vs. all other regions 17555 17555 100 129009
GPe vs. all other regions 17555 17555 100 129012
GPi vs. all other regions 17555 17555 100 129024
V1-1 vs. all other regions 17555 17555 100 128987
V1-2 vs. all other regions 17555 17555 100 128998
V1-3 vs. all other regions 17555 17555 100 128993
V1-4A vs. all other regions 17555 17555 100 128994
V1-4B vs. all other regions 17555 17555 100 128996
V14Ca vs. all other regions 17555 17555 100 129001
V14Cb vs. all other regions 17555 17555 100 129005
V1-5 vs. all other regions 17555 17555 100 129001
V1-6 vs. all other regions 17555 17555 100 129010
V1wm vs. all other regions 17555 17555 100 129026
V2-2 vs. all other regions 17555 17555 100 128996
V2-3 vs. all other regions 17555 17555 100 128991
V2-4 vs. all other regions 17555 17555 100 128996
V2_L5 vs. all other regions 17555 17555 100 128994
NAC vs. all other regions 17555 17555 100 130458
V2-6 vs. all other regions 17555 17555 100 128996
CA2py vs. all other regions 17555 17555 100 128813
DGpf vs. all other regions 17555 17555 100 128794
DGgr vs. all other regions 17555 17555 100 128823
S vs. all other regions 17555 17555 100 128795
CA1py vs. all other regions 17555 17555 100 128807
CA1ra vs. all other regions 17555 17555 100 128809
CA1or vs. all other regions 17555 17555 100 130332
DGsg vs. all other regions 17555 17555 100 128807
CA3py vs. all other regions 17555 17555 100 128804
AA vs. all other regions 17555 17555 100 128696
PL vs. all other regions 17555 17555 100 128700
Me vs. all other regions 17555 17555 100 128681
L vs. all other regions 17555 17555 100 128699
APir vs. all other regions 17555 17555 100 128696
rPAC vs. all other regions 17555 17555 100 128696
AB vs. all other regions 17555 17555 100 128699
AHA vs. all other regions 17555 17555 100 128703
B vs. all other regions 17555 17555 100 128700
CE vs. all other regions 17555 17555 100 128703
CA4 vs. all other regions 17555 17555 100 130333

Run EWCE for each region

## [1] "astrocytes-ependymal"
## [1] 0.69
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.92
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.74
## [1] ""
## [1] "endothelial-mural"
## [1] 0.49
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.36149578773616"
## [1] "Standard deviations from mean: 7.73043290085362"
## [1] ""
## [1] "microglia"
## [1] 0.54
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.48706613495683"
## [1] "Standard deviations from mean: 5.39412006371251"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.3006910218487"
## [1] "Standard deviations from mean: 5.41294367034982"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.30306114510364"
## [1] "Standard deviations from mean: 5.52351082686908"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.82
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.97
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.73
## [1] ""
## [1] "endothelial-mural"
## [1] 0.35
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.3700680438909"
## [1] "Standard deviations from mean: 8.82309287938557"
## [1] ""
## [1] "microglia"
## [1] 0.2
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.33279184192791"
## [1] "Standard deviations from mean: 3.41109767616896"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33815230296478"
## [1] "Standard deviations from mean: 5.98798414409446"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.27629280460224"
## [1] "Standard deviations from mean: 5.37088813639783"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.49
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.88
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.28
## [1] ""
## [1] "endothelial-mural"
## [1] 0.19
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.29227703613989"
## [1] "Standard deviations from mean: 6.19301474960127"
## [1] ""
## [1] "microglia"
## [1] 0.16
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.32885384541189"
## [1] "Standard deviations from mean: 3.80231261579436"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.29004148629611"
## [1] "Standard deviations from mean: 5.18060664161643"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.24766903001379"
## [1] "Standard deviations from mean: 4.60126470944077"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.04
## [1] "Fold enrichment: 1.20971624101054"
## [1] "Standard deviations from mean: 1.81828033235132"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.16
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.27
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.78
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.96
## [1] ""
## [1] "endothelial-mural"
## [1] 0.86
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.50224653938415"
## [1] "Standard deviations from mean: 10.3053919303254"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.33
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.35948375100978"
## [1] "Standard deviations from mean: 6.40297990697599"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.4582057192322"
## [1] "Standard deviations from mean: 8.9655423773847"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.57
## [1] ""
## [1] "endothelial-mural"
## [1] 0.93
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.94
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.81
## [1] ""
## [1] "endothelial-mural"
## [1] 0.78
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.31816169598577"
## [1] "Standard deviations from mean: 6.33899284234282"
## [1] ""
## [1] "microglia"
## [1] 0.23
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.37552893584322"
## [1] "Standard deviations from mean: 4.52055576250819"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.18244959149805"
## [1] "Standard deviations from mean: 3.08959177183019"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.25913868902883"
## [1] "Standard deviations from mean: 5.01154847532714"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.87
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.86
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.64
## [1] ""
## [1] "endothelial-mural"
## [1] 0.23
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.36050804340364"
## [1] "Standard deviations from mean: 7.24795800993131"
## [1] ""
## [1] "microglia"
## [1] 0.18
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.35692691069638"
## [1] "Standard deviations from mean: 4.40146608043808"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.28986462214001"
## [1] "Standard deviations from mean: 5.52065620741554"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.24423875640361"
## [1] "Standard deviations from mean: 4.54606290633971"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.83
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.91
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.65
## [1] ""
## [1] "endothelial-mural"
## [1] 0.76
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.31295017479235"
## [1] "Standard deviations from mean: 6.17871887421864"
## [1] ""
## [1] "microglia"
## [1] 0.62
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.32522978286158"
## [1] "Standard deviations from mean: 3.49998495408187"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33746135195364"
## [1] "Standard deviations from mean: 6.49826352264873"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.29597272196383"
## [1] "Standard deviations from mean: 6.08073710174888"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.67
## [1] ""
## [1] "endothelial-mural"
## [1] 0.99
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.89
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.07
## [1] ""
## [1] "endothelial-mural"
## [1] 0.14
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.22651466651052"
## [1] "Standard deviations from mean: 4.01167772102314"
## [1] ""
## [1] "microglia"
## [1] 0.26
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.43042105463237"
## [1] "Standard deviations from mean: 4.35821391837049"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.24786678263757"
## [1] "Standard deviations from mean: 4.37249239022044"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.23438255030681"
## [1] "Standard deviations from mean: 4.56287400363116"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.75
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.91
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.97
## [1] ""
## [1] "endothelial-mural"
## [1] 0.91
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.43380662021178"
## [1] "Standard deviations from mean: 7.73867774850157"
## [1] ""
## [1] "microglia"
## [1] 0.97
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.3834688946679"
## [1] "Standard deviations from mean: 3.8713145386966"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.35458476242393"
## [1] "Standard deviations from mean: 6.50205763876491"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.3680353776727"
## [1] "Standard deviations from mean: 6.14124278687038"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.83
## [1] ""
## [1] "endothelial-mural"
## [1] 0.99
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.9
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.89
## [1] ""
## [1] "endothelial-mural"
## [1] 0.73
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.44843776981431"
## [1] "Standard deviations from mean: 8.96532085343343"
## [1] ""
## [1] "microglia"
## [1] 0.59
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.37377953042244"
## [1] "Standard deviations from mean: 4.57305150798601"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.31812101054864"
## [1] "Standard deviations from mean: 5.22532442557582"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.33090714349838"
## [1] "Standard deviations from mean: 5.80603267628215"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.84
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.95
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.84
## [1] ""
## [1] "endothelial-mural"
## [1] 0.97
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.34759803172799"
## [1] "Standard deviations from mean: 6.79346066332768"
## [1] ""
## [1] "microglia"
## [1] 0.51
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.25766584745416"
## [1] "Standard deviations from mean: 2.56206833777982"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.36339185402269"
## [1] "Standard deviations from mean: 7.11731453956082"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.3373779339942"
## [1] "Standard deviations from mean: 6.45608084307466"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.82
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.95
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.82
## [1] ""
## [1] "endothelial-mural"
## [1] 0.73
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.38137625614076"
## [1] "Standard deviations from mean: 7.51625656165242"
## [1] ""
## [1] "microglia"
## [1] 0.66
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.03
## [1] "Fold enrichment: 1.25493492942026"
## [1] "Standard deviations from mean: 2.40834151792195"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.39930295518793"
## [1] "Standard deviations from mean: 6.81153007756045"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.39106929297621"
## [1] "Standard deviations from mean: 7.78880036008649"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.11
## [1] ""
## [1] "endothelial-mural"
## [1] 0.43
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.66
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.69
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.99
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.5621222004961"
## [1] "Standard deviations from mean: 12.5150093541627"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.43
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.40467918113106"
## [1] "Standard deviations from mean: 6.80783994245793"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.45913555622893"
## [1] "Standard deviations from mean: 8.29765048082985"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.66
## [1] ""
## [1] "endothelial-mural"
## [1] 0.99
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.87
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.74
## [1] ""
## [1] "endothelial-mural"
## [1] 0.79
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.44795242248311"
## [1] "Standard deviations from mean: 8.45665807903486"
## [1] ""
## [1] "microglia"
## [1] 0.71
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.51039283321374"
## [1] "Standard deviations from mean: 5.93001116533696"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.32773122803151"
## [1] "Standard deviations from mean: 5.67429368859355"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.36909390523736"
## [1] "Standard deviations from mean: 5.91227369102989"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.78
## [1] ""
## [1] "endothelial-mural"
## [1] 0.95
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.79
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.61
## [1] ""
## [1] "endothelial-mural"
## [1] 0.36
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.39486214796231"
## [1] "Standard deviations from mean: 7.37205193558631"
## [1] ""
## [1] "microglia"
## [1] 0.12
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.24482744229959"
## [1] "Standard deviations from mean: 2.53644643417514"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.28943834448305"
## [1] "Standard deviations from mean: 6.23199336799741"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.21958585957237"
## [1] "Standard deviations from mean: 4.64186024386257"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.75
## [1] ""
## [1] "endothelial-mural"
## [1] 0.98
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.92
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.36
## [1] ""
## [1] "endothelial-mural"
## [1] 0.13
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.30844483976371"
## [1] "Standard deviations from mean: 6.24877224873451"
## [1] ""
## [1] "microglia"
## [1] 0.57
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.34055306650585"
## [1] "Standard deviations from mean: 3.75054657923674"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33344011129399"
## [1] "Standard deviations from mean: 5.6710161103249"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.2992959543193"
## [1] "Standard deviations from mean: 5.77464691151524"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.31
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.85
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.59
## [1] ""
## [1] "endothelial-mural"
## [1] 0.59
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.45991365654631"
## [1] "Standard deviations from mean: 8.33501477494695"
## [1] ""
## [1] "microglia"
## [1] 0.76
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.25
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33185773870414"
## [1] "Standard deviations from mean: 5.27417113038539"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.35989210449217"
## [1] "Standard deviations from mean: 6.03018789238473"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.15
## [1] ""
## [1] "endothelial-mural"
## [1] 0.79
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.62
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.79
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.84
## [1] ""
## [1] "endothelial-mural"
## [1] 0.93
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.49085975083854"
## [1] "Standard deviations from mean: 10.2236170140348"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.57
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.32092329330656"
## [1] "Standard deviations from mean: 5.88231463180095"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.3579236911614"
## [1] "Standard deviations from mean: 6.5865384050543"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.13
## [1] ""
## [1] "endothelial-mural"
## [1] 0.15
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.54
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.47
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.98
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.95
## [1] ""
## [1] "endothelial-mural"
## [1] 0.94
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.38997916859193"
## [1] "Standard deviations from mean: 8.11429841638261"
## [1] ""
## [1] "microglia"
## [1] 0.51
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.01
## [1] "Fold enrichment: 1.17332537916424"
## [1] "Standard deviations from mean: 1.90264302432939"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.32356707887264"
## [1] "Standard deviations from mean: 6.09647703284844"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.31144411926353"
## [1] "Standard deviations from mean: 5.94006264918281"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.06
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.38551373175015"
## [1] "Standard deviations from mean: 2.92079510642893"
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.01
## [1] "Fold enrichment: 1.46018622778369"
## [1] "Standard deviations from mean: 2.57631933103226"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 2.16112033661971"
## [1] "Standard deviations from mean: 13.3692834699465"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.57188130086226"
## [1] "Standard deviations from mean: 11.3201395537844"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.55138764316653"
## [1] "Standard deviations from mean: 10.1874641509226"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.67894614152506"
## [1] "Standard deviations from mean: 13.4887628005985"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.4031458891024"
## [1] "Standard deviations from mean: 3.13677762894169"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.13
## [1] ""
## [1] "interneurons"
## [1] 0.93
## [1] ""
## [1] "microglia"
## [1] 0.72
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.89
## [1] ""
## [1] "endothelial-mural"
## [1] 0.44
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.27628832509747"
## [1] "Standard deviations from mean: 5.2235438343547"
## [1] ""
## [1] "microglia"
## [1] 0.54
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.29
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.38776064644479"
## [1] "Standard deviations from mean: 6.3662912677179"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.55067257381571"
## [1] "Standard deviations from mean: 9.51482252806637"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.06
## [1] ""
## [1] "endothelial-mural"
## [1] 0.09
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.47
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.12
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.76
## [1] ""
## [1] "endothelial-mural"
## [1] 0.97
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.37379638148696"
## [1] "Standard deviations from mean: 6.2052097514887"
## [1] ""
## [1] "microglia"
## [1] 0.84
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.28
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.23137724758419"
## [1] "Standard deviations from mean: 3.76432407243025"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.41765917241121"
## [1] "Standard deviations from mean: 7.30602505474287"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.12
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.32
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.94
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.97
## [1] ""
## [1] "pyramidal SS"
## [1] 0.99
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.85
## [1] ""
## [1] "endothelial-mural"
## [1] 0.78
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.33845119170987"
## [1] "Standard deviations from mean: 6.47556238057059"
## [1] ""
## [1] "microglia"
## [1] 0.84
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.08
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.30421348835203"
## [1] "Standard deviations from mean: 5.3959306894098"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.43080122001797"
## [1] "Standard deviations from mean: 7.41109266741336"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.01
## [1] "Fold enrichment: 1.31319351819271"
## [1] "Standard deviations from mean: 2.48344244904012"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.01
## [1] "Fold enrichment: 1.38462282582869"
## [1] "Standard deviations from mean: 3.06937544847777"
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.01
## [1] "Fold enrichment: 1.43441833751683"
## [1] "Standard deviations from mean: 2.58739155741733"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.89821029534966"
## [1] "Standard deviations from mean: 10.034880972535"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.91
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.24251646983205"
## [1] "Standard deviations from mean: 4.54464068539368"
## [1] ""
## [1] "microglia"
## [1] 0.98
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.76753524229695"
## [1] "Standard deviations from mean: 13.1607758421138"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.79677233574885"
## [1] "Standard deviations from mean: 12.9741861694883"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.3639194869393"
## [1] "Standard deviations from mean: 3.01911792253967"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.49382403494025"
## [1] "Standard deviations from mean: 3.62526766796914"
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0
## [1] "Fold enrichment: 1.58106408175289"
## [1] "Standard deviations from mean: 3.35463193938967"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.85819896588427"
## [1] "Standard deviations from mean: 7.83543414462605"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.31058239027344"
## [1] "Standard deviations from mean: 6.24780704095568"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.83070120165711"
## [1] "Standard deviations from mean: 14.6665482503819"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.68803526349134"
## [1] "Standard deviations from mean: 13.4787845822556"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.52609664045784"
## [1] "Standard deviations from mean: 4.19083836259959"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.29726131132156"
## [1] "Standard deviations from mean: 2.59075735818402"
## [1] ""
## [1] "interneurons"
## [1] 0.97
## [1] ""
## [1] "microglia"
## [1] 0.26
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.87
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.53005309432026"
## [1] "Standard deviations from mean: 10.8629930557857"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.38
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.47859459775011"
## [1] "Standard deviations from mean: 8.52171671270359"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.46840477664178"
## [1] "Standard deviations from mean: 9.45872288692324"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.24
## [1] ""
## [1] "endothelial-mural"
## [1] 0.6
## [1] ""
## [1] "interneurons"
## [1] 0.75
## [1] ""
## [1] "microglia"
## [1] 0.93
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.97
## [1] ""
## [1] "pyramidal SS"
## [1] 0.96
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.01
## [1] "Fold enrichment: 1.27875253978408"
## [1] "Standard deviations from mean: 2.21498627935773"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.19
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.16510157978425"
## [1] "Standard deviations from mean: 3.0757711352291"
## [1] ""
## [1] "microglia"
## [1] 0.06
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.61898283171582"
## [1] "Standard deviations from mean: 6.42824918717225"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.02
## [1] "Fold enrichment: 1.14738623521404"
## [1] "Standard deviations from mean: 2.43351059970434"
## [1] ""
## [1] "pyramidal SS"
## [1] 0.06
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.06
## [1] ""
## [1] "endothelial-mural"
## [1] 0.71
## [1] ""
## [1] "interneurons"
## [1] 0.52
## [1] ""
## [1] "microglia"
## [1] 0.96
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.98
## [1] ""
## [1] "pyramidal SS"
## [1] 0.46
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.02
## [1] "Fold enrichment: 1.27013492529625"
## [1] "Standard deviations from mean: 2.53201931850234"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.36960966205838"
## [1] "Standard deviations from mean: 3.44243983266395"
## [1] ""
## [1] "interneurons"
## [1] 0.02
## [1] "Fold enrichment: 1.10602440785071"
## [1] "Standard deviations from mean: 1.89147014239509"
## [1] ""
## [1] "microglia"
## [1] 0.17
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.48260197129409"
## [1] "Standard deviations from mean: 4.94510810352423"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.15221985187446"
## [1] "Standard deviations from mean: 2.64064710896877"
## [1] ""
## [1] "pyramidal SS"
## [1] 0.06
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.05
## [1] ""
## [1] "endothelial-mural"
## [1] 0.77
## [1] ""
## [1] "interneurons"
## [1] 0.95
## [1] ""
## [1] "microglia"
## [1] 0.81
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.95
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.99
## [1] ""
## [1] "pyramidal SS"
## [1] 0.6
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.16
## [1] ""
## [1] "endothelial-mural"
## [1] 0.52
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.33704865099281"
## [1] "Standard deviations from mean: 7.40613869834812"
## [1] ""
## [1] "microglia"
## [1] 0.64
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.33537098255361"
## [1] "Standard deviations from mean: 3.31012553162413"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.38474998404645"
## [1] "Standard deviations from mean: 6.57224942160014"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.28697578921463"
## [1] "Standard deviations from mean: 5.16826741412223"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.03
## [1] "Fold enrichment: 1.20956718731157"
## [1] "Standard deviations from mean: 2.01098547163833"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.29
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.68
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.15
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.79
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.38
## [1] ""
## [1] "endothelial-mural"
## [1] 0.78
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.38636567606745"
## [1] "Standard deviations from mean: 7.27021212077539"
## [1] ""
## [1] "microglia"
## [1] 0.89
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.04
## [1] "Fold enrichment: 1.17538494431609"
## [1] "Standard deviations from mean: 1.84240286560029"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.42473904666186"
## [1] "Standard deviations from mean: 7.55860337421488"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.30049635743458"
## [1] "Standard deviations from mean: 5.45693806525425"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.08
## [1] ""
## [1] "endothelial-mural"
## [1] 0.39
## [1] ""
## [1] "interneurons"
## [1] 0.97
## [1] ""
## [1] "microglia"
## [1] 0.81
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.79
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.5
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.4
## [1] ""
## [1] "endothelial-mural"
## [1] 0.8
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.29144465882492"
## [1] "Standard deviations from mean: 6.22949262910354"
## [1] ""
## [1] "microglia"
## [1] 0.61
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.05
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.46970065256005"
## [1] "Standard deviations from mean: 7.94294569981396"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.30390038215338"
## [1] "Standard deviations from mean: 6.27551010597199"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.05
## [1] ""
## [1] "endothelial-mural"
## [1] 0.65
## [1] ""
## [1] "interneurons"
## [1] 0.94
## [1] ""
## [1] "microglia"
## [1] 0.66
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.99
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.99
## [1] ""
## [1] "pyramidal SS"
## [1] 0.12
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.54
## [1] ""
## [1] "endothelial-mural"
## [1] 0.96
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.31394892835994"
## [1] "Standard deviations from mean: 6.1173938461333"
## [1] ""
## [1] "microglia"
## [1] 0.48
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.13
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.44779456916031"
## [1] "Standard deviations from mean: 8.22710021064428"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.29153853239118"
## [1] "Standard deviations from mean: 5.10817757482449"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.32915673278872"
## [1] "Standard deviations from mean: 2.63273952641479"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.62
## [1] ""
## [1] "interneurons"
## [1] 0.99
## [1] ""
## [1] "microglia"
## [1] 0.93
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.92
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.72
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.07
## [1] ""
## [1] "endothelial-mural"
## [1] 0.11
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.30287907402794"
## [1] "Standard deviations from mean: 6.40418355880191"
## [1] ""
## [1] "microglia"
## [1] 0.28
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.37619185813792"
## [1] "Standard deviations from mean: 3.88909548278151"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.34925567557713"
## [1] "Standard deviations from mean: 6.16835467328472"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.20523887570248"
## [1] "Standard deviations from mean: 3.73047543861585"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.02
## [1] "Fold enrichment: 1.2345078048121"
## [1] "Standard deviations from mean: 2.00325776182715"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.39
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.79
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.91
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.87
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.01
## [1] "Fold enrichment: 1.42996639958263"
## [1] "Standard deviations from mean: 3.28211202146089"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.01
## [1] "Fold enrichment: 1.2803790310865"
## [1] "Standard deviations from mean: 2.40068779294221"
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.25313038455265"
## [1] "Standard deviations from mean: 5.3863020655235"
## [1] ""
## [1] "microglia"
## [1] 0.18
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.44535137179669"
## [1] "Standard deviations from mean: 4.90458785086483"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.29068397841707"
## [1] "Standard deviations from mean: 4.93727519251717"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.16841017166969"
## [1] "Standard deviations from mean: 3.46700544574065"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.36696360234368"
## [1] "Standard deviations from mean: 2.93024584295815"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.41794909698978"
## [1] "Standard deviations from mean: 3.17077121039667"
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0
## [1] "Fold enrichment: 1.594983480188"
## [1] "Standard deviations from mean: 3.86912857865004"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 2.09605843990363"
## [1] "Standard deviations from mean: 11.9798183878005"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.55698264119325"
## [1] "Standard deviations from mean: 10.6010692407944"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.67180991235752"
## [1] "Standard deviations from mean: 11.3869500487921"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.66022158463122"
## [1] "Standard deviations from mean: 11.5082549610787"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.15
## [1] ""
## [1] "endothelial-mural"
## [1] 0.57
## [1] ""
## [1] "interneurons"
## [1] 0.93
## [1] ""
## [1] "microglia"
## [1] 0.95
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.96
## [1] ""
## [1] "pyramidal SS"
## [1] 0.91
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.19
## [1] ""
## [1] "endothelial-mural"
## [1] 0.01
## [1] "Fold enrichment: 1.40057184391778"
## [1] "Standard deviations from mean: 2.55313146551903"
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0
## [1] "Fold enrichment: 1.39996186724745"
## [1] "Standard deviations from mean: 2.31039974252778"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.06
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.04
## [1] "Fold enrichment: 1.21362653994441"
## [1] "Standard deviations from mean: 1.73082803952873"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.47
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.8
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.97
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.89
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.09
## [1] ""
## [1] "endothelial-mural"
## [1] 0.28
## [1] ""
## [1] "interneurons"
## [1] 0.97
## [1] ""
## [1] "microglia"
## [1] 0.24
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.09
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.46
## [1] ""
## [1] "pyramidal SS"
## [1] 0.91
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.07
## [1] ""
## [1] "endothelial-mural"
## [1] 0.75
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.9
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.48
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.62
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.08
## [1] ""
## [1] "endothelial-mural"
## [1] 0.45
## [1] ""
## [1] "interneurons"
## [1] 0.73
## [1] ""
## [1] "microglia"
## [1] 0.05
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.86
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.11946937833408"
## [1] "Standard deviations from mean: 2.34446131585096"
## [1] ""
## [1] "pyramidal SS"
## [1] 0.37
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.14
## [1] ""
## [1] "endothelial-mural"
## [1] 0.19
## [1] ""
## [1] "interneurons"
## [1] 0.99
## [1] ""
## [1] "microglia"
## [1] 0.84
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.4
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.9
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.23
## [1] ""
## [1] "endothelial-mural"
## [1] 0.05
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.08
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.72
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.9
## [1] ""
## [1] "pyramidal SS"
## [1] 0.92
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.05
## [1] ""
## [1] "endothelial-mural"
## [1] 0.17
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.37
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.95
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.94
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.94
## [1] ""
## [1] "endothelial-mural"
## [1] 0.84
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.32337264890325"
## [1] "Standard deviations from mean: 6.2116837511867"
## [1] ""
## [1] "microglia"
## [1] 0.79
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.27508029866987"
## [1] "Standard deviations from mean: 2.8786375666947"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.30343902889344"
## [1] "Standard deviations from mean: 4.93204992614422"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.43055354120573"
## [1] "Standard deviations from mean: 8.31947139445578"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.14
## [1] ""
## [1] "endothelial-mural"
## [1] 0.24
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.76
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.01
## [1] "Fold enrichment: 1.26223151161346"
## [1] "Standard deviations from mean: 2.59043133720525"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.99
## [1] ""
## [1] "pyramidal SS"
## [1] 0.98
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.36870464124298"
## [1] "Standard deviations from mean: 3.34394567074562"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.16
## [1] ""
## [1] "interneurons"
## [1] 0.73
## [1] ""
## [1] "microglia"
## [1] 0.74
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.7
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.78
## [1] ""
## [1] "pyramidal SS"
## [1] 0.97
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.34115719802433"
## [1] "Standard deviations from mean: 2.90876896293013"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.34
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.95
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.99
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.58
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.31
## [1] ""
## [1] "endothelial-mural"
## [1] 0.01
## [1] "Fold enrichment: 1.3627028838159"
## [1] "Standard deviations from mean: 2.70302009986228"
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.31537620067647"
## [1] "Standard deviations from mean: 5.98986609686851"
## [1] ""
## [1] "microglia"
## [1] 0.2
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.33364386151528"
## [1] "Standard deviations from mean: 4.02450240208077"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.01
## [1] "Fold enrichment: 1.13076377124095"
## [1] "Standard deviations from mean: 2.51095789921836"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.28925562490391"
## [1] "Standard deviations from mean: 5.61668594748418"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.4823749819384"
## [1] "Standard deviations from mean: 3.93833960224507"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.65
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.97
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.82
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.39908119770173"
## [1] "Standard deviations from mean: 8.20790986568399"
## [1] ""
## [1] "microglia"
## [1] 0.99
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.32
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.47412414157004"
## [1] "Standard deviations from mean: 7.84349015856196"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.49911975638761"
## [1] "Standard deviations from mean: 9.56659473537242"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.03
## [1] "Fold enrichment: 1.2633411821024"
## [1] "Standard deviations from mean: 2.25059962967349"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.43
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.92
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.67
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 2.34438698015671"
## [1] "Standard deviations from mean: 10.2850431979544"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 2.20934908576778"
## [1] "Standard deviations from mean: 10.4605140577291"
## [1] ""
## [1] "interneurons"
## [1] 0.3
## [1] ""
## [1] "microglia"
## [1] 0.04
## [1] "Fold enrichment: 1.41271134947188"
## [1] "Standard deviations from mean: 2.3107487415948"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.46441253180761"
## [1] "Standard deviations from mean: 4.68129096454793"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.97
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.03
## [1] "Fold enrichment: 1.25848007054243"
## [1] "Standard deviations from mean: 2.02384462845744"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.43
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.86
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.9
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.95
## [1] ""
## [1] "pyramidal SS"
## [1] 0.99
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.9
## [1] ""
## [1] "endothelial-mural"
## [1] 0.43
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.30008675377373"
## [1] "Standard deviations from mean: 5.42031403715552"
## [1] ""
## [1] "microglia"
## [1] 0.54
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.06
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.28613948352802"
## [1] "Standard deviations from mean: 4.92855167221698"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.28062017245432"
## [1] "Standard deviations from mean: 5.31129428111827"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.94
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.98
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.11447815932378"
## [1] "Standard deviations from mean: 2.09159270771025"
## [1] ""
## [1] "pyramidal SS"
## [1] 0.99
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.53
## [1] ""
## [1] "endothelial-mural"
## [1] 0.1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.2567000331111"
## [1] "Standard deviations from mean: 5.823761815847"
## [1] ""
## [1] "microglia"
## [1] 0.64
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.04
## [1] "Fold enrichment: 1.14249586110947"
## [1] "Standard deviations from mean: 1.67251676999416"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.65
## [1] ""
## [1] "pyramidal SS"
## [1] 0.06
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.41971612339772"
## [1] "Standard deviations from mean: 3.51058535564215"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.35
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.2
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.89
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.98
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.4150074517071"
## [1] "Standard deviations from mean: 8.5208885544443"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.26
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.41655204777831"
## [1] "Standard deviations from mean: 7.16171857119791"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.4073703625943"
## [1] "Standard deviations from mean: 7.96075859275961"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.2578688324965"
## [1] "Standard deviations from mean: 2.52283635811912"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.33
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.09
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.34124327189425"
## [1] "Standard deviations from mean: 3.39495369391125"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.99
## [1] ""
## [1] "endothelial-mural"
## [1] 0.98
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.43538308689614"
## [1] "Standard deviations from mean: 8.39895492264893"
## [1] ""
## [1] "microglia"
## [1] 0.98
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.68
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.45138433967613"
## [1] "Standard deviations from mean: 7.92033419798832"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.48043339961085"
## [1] "Standard deviations from mean: 9.22131641168356"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.38763056109799"
## [1] "Standard deviations from mean: 3.67978337177896"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.31
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.93
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.45
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.41037174922249"
## [1] "Standard deviations from mean: 3.90731252645131"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.91497462930138"
## [1] "Standard deviations from mean: 7.03185915983936"
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.28375074944581"
## [1] "Standard deviations from mean: 5.59750081235203"
## [1] ""
## [1] "microglia"
## [1] 0.33
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.09
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.44
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.18402102170687"
## [1] "Standard deviations from mean: 3.18818693941427"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.01
## [1] "Fold enrichment: 1.30998778171148"
## [1] "Standard deviations from mean: 2.65563038799159"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.68
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.94
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.97
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.84
## [1] ""
## [1] "endothelial-mural"
## [1] 0.14
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.30643621630414"
## [1] "Standard deviations from mean: 6.28323424726205"
## [1] ""
## [1] "microglia"
## [1] 0.75
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.42
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.08
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.22096190760641"
## [1] "Standard deviations from mean: 4.20758763015106"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.4233602033347"
## [1] "Standard deviations from mean: 3.34655526057046"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.4
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.53
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.98
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.99
## [1] ""
## [1] "endothelial-mural"
## [1] 0.95
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.37142565889519"
## [1] "Standard deviations from mean: 7.61821600874018"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.13
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.5281425836805"
## [1] "Standard deviations from mean: 9.34630109808363"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.60026886047134"
## [1] "Standard deviations from mean: 11.5171999874655"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.40351045744542"
## [1] "Standard deviations from mean: 3.02864627650639"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.79
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.86
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.56
## [1] ""
## [1] "endothelial-mural"
## [1] 0.94
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.43321983170287"
## [1] "Standard deviations from mean: 8.98127431405562"
## [1] ""
## [1] "microglia"
## [1] 0.99
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.02
## [1] "Fold enrichment: 1.18443743673693"
## [1] "Standard deviations from mean: 2.09424180798725"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.25079983630804"
## [1] "Standard deviations from mean: 4.1592618720545"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.33007645619501"
## [1] "Standard deviations from mean: 6.20145618524968"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.43902964300419"
## [1] "Standard deviations from mean: 3.51203374583504"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.81
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.8
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.97
## [1] ""
## [1] "endothelial-mural"
## [1] 0.96
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.3527570522622"
## [1] "Standard deviations from mean: 6.4564748117626"
## [1] ""
## [1] "microglia"
## [1] 0.99
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.01
## [1] "Fold enrichment: 1.21787848660516"
## [1] "Standard deviations from mean: 2.40407228399115"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.50680502603419"
## [1] "Standard deviations from mean: 9.74149985433321"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.60480848128911"
## [1] "Standard deviations from mean: 12.3279086990675"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.30165031481362"
## [1] "Standard deviations from mean: 2.91894882639394"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.27
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.96
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.99
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.91
## [1] ""
## [1] "endothelial-mural"
## [1] 0.74
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.43562743942985"
## [1] "Standard deviations from mean: 8.29866369629902"
## [1] ""
## [1] "microglia"
## [1] 0.98
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.19
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33514251994228"
## [1] "Standard deviations from mean: 5.80942661119468"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.30381838199839"
## [1] "Standard deviations from mean: 5.68950336488475"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.49086306422457"
## [1] "Standard deviations from mean: 4.28604372228488"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.42
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.84
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.8
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.88
## [1] ""
## [1] "endothelial-mural"
## [1] 0.95
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.34612727454254"
## [1] "Standard deviations from mean: 6.59714653108469"
## [1] ""
## [1] "microglia"
## [1] 0.94
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.11
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.27915196135261"
## [1] "Standard deviations from mean: 5.05761409564878"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.38152487175855"
## [1] "Standard deviations from mean: 7.27000855974018"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.41975936273514"
## [1] "Standard deviations from mean: 3.98120346291382"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.25
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.92
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.83
## [1] ""
## [1] "endothelial-mural"
## [1] 0.91
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.39864057422312"
## [1] "Standard deviations from mean: 8.61198427654143"
## [1] ""
## [1] "microglia"
## [1] 0.98
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.01
## [1] "Fold enrichment: 1.22448644954557"
## [1] "Standard deviations from mean: 2.44343527043834"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.34649957554668"
## [1] "Standard deviations from mean: 6.85332191081762"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.35156421103183"
## [1] "Standard deviations from mean: 6.30664236799328"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.06
## [1] ""
## [1] "endothelial-mural"
## [1] 0.96
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.99
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.92
## [1] ""
## [1] "endothelial-mural"
## [1] 0.76
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.35758518927623"
## [1] "Standard deviations from mean: 7.00350624455461"
## [1] ""
## [1] "microglia"
## [1] 0.99
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.04
## [1] "Fold enrichment: 1.20246503521053"
## [1] "Standard deviations from mean: 1.8767100555157"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.24454338653484"
## [1] "Standard deviations from mean: 4.05436476036237"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.30299518195013"
## [1] "Standard deviations from mean: 5.11873168238985"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.44621033351169"
## [1] "Standard deviations from mean: 3.52925041663019"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.5
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.97
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.92
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.30524616238526"
## [1] "Standard deviations from mean: 5.57754542390505"
## [1] ""
## [1] "microglia"
## [1] 0.93
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.01
## [1] "Fold enrichment: 1.19110345839714"
## [1] "Standard deviations from mean: 2.36978916297784"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33112906211396"
## [1] "Standard deviations from mean: 5.80698816967745"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.43015870269901"
## [1] "Standard deviations from mean: 8.12654218634643"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.37710612615752"
## [1] "Standard deviations from mean: 3.25072805984142"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.44
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.64
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.99
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.87
## [1] ""
## [1] "endothelial-mural"
## [1] 0.66
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.44891705400785"
## [1] "Standard deviations from mean: 8.76072691559011"
## [1] ""
## [1] "microglia"
## [1] 0.94
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.05
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.32038508387217"
## [1] "Standard deviations from mean: 5.83248576316005"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.37374233436999"
## [1] "Standard deviations from mean: 6.34668263502661"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.44022400912604"
## [1] "Standard deviations from mean: 3.55072306146624"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.48
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.85
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.8
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.98
## [1] ""
## [1] "endothelial-mural"
## [1] 0.97
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.40709698005332"
## [1] "Standard deviations from mean: 8.09206400945743"
## [1] ""
## [1] "microglia"
## [1] 0.96
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.26
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.47119020717666"
## [1] "Standard deviations from mean: 8.57557975114125"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.58380281696041"
## [1] "Standard deviations from mean: 10.5441974474243"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.01
## [1] "Fold enrichment: 1.21903708533958"
## [1] "Standard deviations from mean: 1.87730072497057"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.42
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.97
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.67
## [1] ""
## [1] "endothelial-mural"
## [1] 0.31
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.27071434528459"
## [1] "Standard deviations from mean: 5.2285046862428"
## [1] ""
## [1] "microglia"
## [1] 0.45
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.02
## [1] "Fold enrichment: 1.19564719049588"
## [1] "Standard deviations from mean: 2.17454988211899"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.14900771945043"
## [1] "Standard deviations from mean: 2.71585040616355"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.24214820357332"
## [1] "Standard deviations from mean: 4.08714486088249"
## [1] ""

Subset regions enriched in certain cell types

cell.type1 <-"astrocytes-ependymal"
cell.type2 <-"oligodendrocytes"
cell.type3 <-"interneurons"

enrichment.summary <-data.frame()
for(region in region_list){
  # 1st cell type
  if(subset(tt_results[[region]]$joint_results, CellType==cell.type1 &
            Direction=="Up")$p<=0.05){enriched1="Yes"} else{enriched1="No"}
  # 2nd cell type
    if(subset(tt_results[[region]]$joint_results, CellType==cell.type2 &
            Direction=="Up")$p<=0.05){enriched2="Yes"} else{enriched2="No"}
  # 3rd cell type
    if(subset(tt_results[[region]]$joint_results, CellType==cell.type3 &
            Direction=="Up")$p<=0.05){enriched3="Yes"} else{enriched3="No"}
  # Summarise
  enrichment.summary <-rbind(enrichment.summary, 
            data.frame(Region=region, Cell.Type=cell.type1, Enriched=enriched1), 
            data.frame(Region=region, Cell.Type=cell.type2, Enriched=enriched2),
            data.frame(Region=region, Cell.Type=cell.type3, Enriched=enriched3))
}

# Get only significantly enriched regions
enrich.summ <-subset(enrichment.summary, Enriched=="Yes")
pander(enrich.summ[order(enrich.summ$Region),], style='simple',justify='left')
  Region Cell.Type Enriched
10 rCG6 astrocytes-ependymal Yes
59 ic oligodendrocytes Yes
61 IsCj astrocytes-ependymal Yes
70 GPe astrocytes-ependymal Yes
71 GPe oligodendrocytes Yes
73 GPi astrocytes-ependymal Yes
74 GPi oligodendrocytes Yes
76 V1-1 astrocytes-ependymal Yes
85 V1-4A astrocytes-ependymal Yes
88 V1-4B astrocytes-ependymal Yes
94 V14Cb astrocytes-ependymal Yes
97 V1-5 astrocytes-ependymal Yes
100 V1-6 astrocytes-ependymal Yes
103 V1wm astrocytes-ependymal Yes
104 V1wm oligodendrocytes Yes
109 V2-3 astrocytes-ependymal Yes
118 NAC astrocytes-ependymal Yes
122 V2-6 oligodendrocytes Yes
124 CA2py astrocytes-ependymal Yes
127 DGpf astrocytes-ependymal Yes
130 DGgr astrocytes-ependymal Yes
133 S astrocytes-ependymal Yes
139 CA1ra astrocytes-ependymal Yes
142 CA1or astrocytes-ependymal Yes
143 CA1or oligodendrocytes Yes
145 DGsg astrocytes-ependymal Yes
148 CA3py astrocytes-ependymal Yes
151 AA astrocytes-ependymal Yes
154 PL astrocytes-ependymal Yes
157 Me astrocytes-ependymal Yes
160 L astrocytes-ependymal Yes
163 APir astrocytes-ependymal Yes
166 rPAC astrocytes-ependymal Yes
172 AHA astrocytes-ependymal Yes
175 B astrocytes-ependymal Yes
178 CE astrocytes-ependymal Yes
181 CA4 astrocytes-ependymal Yes